From 46e3141c276938fc3473657adf66bdce045b997b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pablo=20Ulloa=20Sant=C3=ADn?= Date: Mon, 19 Oct 2026 14:52:18 +0200 Subject: [PATCH 1/3] feat: add websites and automatic PyPI publishing Add the landing, documentation and playground sites with shared eshkra branding, tested builds and Cloudflare deployment from main. Publish to PyPI automatically when a push to main changes project.version, using the CI-built distributions and the pypi_env token. Move schema inference to mlschema 0.3 and prepare version 0.2.0. --- .github/workflows/ci.yml | 18 + .github/workflows/deploy-sites.yml | 71 + .github/workflows/publish.yml | 31 +- .github/workflows/sites.yml | 77 + .gitignore | 6 + .pre-commit-config.yaml | 3 + AGENTS.md | 492 +- README.md | 469 +- SECURITY.md | 5 +- brand/README.md | 14 + brand/mlbundle.png | Bin 0 -> 961135 bytes brand/mona-sans-LICENSE.txt | 93 + brand/wordmark-dark.svg | 14 + brand/wordmark-light.svg | 14 + docs/.gitignore | 3 + docs/README.md | 35 + docs/astro.config.ts | 73 + docs/deployment.md | 85 + docs/format-pipeline.md | 2 +- docs/format-schema.md | 16 +- docs/format-state.md | 4 +- docs/format.md | 4 +- docs/package.json | 27 + docs/publishing.md | 103 +- docs/src/components/BrandHead.astro | 19 + docs/src/components/BrandTitle.astro | 54 + docs/src/components/Footer.astro | 41 + docs/src/components/SocialIcons.astro | 54 + docs/src/components/SpecPage.astro | 31 + docs/src/content.config.ts | 33 + docs/src/content/docs/adapters/catboost.md | 52 + docs/src/content/docs/adapters/onnx.md | 59 + docs/src/content/docs/adapters/overview.md | 39 + docs/src/content/docs/adapters/pipelines.md | 66 + docs/src/content/docs/adapters/sklearn.md | 83 + docs/src/content/docs/adapters/xgboost.md | 60 + .../docs/getting-started/installation.md | 87 + .../docs/getting-started/quick-start.md | 100 + docs/src/content/docs/guides/cli.md | 73 + docs/src/content/docs/guides/export.md | 144 + .../content/docs/guides/inspect-and-load.md | 112 + docs/src/content/docs/guides/records.md | 126 + .../content/docs/guides/schema-and-samples.md | 158 + docs/src/content/docs/guides/trust.md | 66 + docs/src/content/docs/index.md | 89 + docs/src/content/docs/reference/api.md | 141 + .../src/content/docs/reference/development.md | 49 + docs/src/content/docs/reference/errors.md | 69 + docs/src/content/docs/reference/limits.md | 58 + docs/src/fonts/jetbrains-mono-LICENSE.txt | 93 + ...jetbrains-mono-latin-ext-wght-normal.woff2 | Bin 0 -> 15196 bytes .../jetbrains-mono-latin-wght-normal.woff2 | Bin 0 -> 40404 bytes docs/src/fonts/mona-sans-LICENSE.txt | 93 + .../mona-sans-latin-ext-standard-normal.woff2 | Bin 0 -> 32800 bytes .../mona-sans-latin-standard-normal.woff2 | Bin 0 -> 98124 bytes docs/src/navigation.ts | 48 + docs/src/pages/format/[...part].astro | 17 + docs/src/pages/security.astro | 5 + docs/src/sites.ts | 5 + docs/src/spec.test.ts | 49 + docs/src/spec.ts | 49 + docs/src/styles/content.css | 134 + docs/src/styles/home.css | 77 + docs/src/styles/theme.css | 171 + docs/src/styles/typography.css | 54 + docs/tsconfig.json | 5 + docs/wrangler.jsonc | 12 + landing/.gitignore | 4 + landing/.prettierignore | 6 + landing/.prettierrc.json | 7 + landing/README.md | 46 + landing/astro.config.ts | 19 + landing/examples/train.py | 24 + landing/package.json | 28 + landing/public/_headers | 3 + landing/public/licenses/jetbrains-mono.txt | 93 + landing/public/licenses/mona-sans.txt | 93 + landing/public/robots.txt | 4 + landing/public/sitemap.xml | 4 + landing/ruff.toml | 7 + landing/scripts/example.py | 90 + landing/src/components/BundleCard.astro | 36 + landing/src/components/Formats.astro | 54 + landing/src/components/Hero.astro | 93 + landing/src/components/Icon.astro | 45 + landing/src/components/Inside.astro | 47 + landing/src/components/Schema.astro | 49 + landing/src/components/Start.astro | 41 + landing/src/components/Terminal.astro | 19 + landing/src/components/Verify.astro | 47 + landing/src/components/Wordmark.astro | 12 + landing/src/data/example.json | 180 + landing/src/data/example.ts | 32 + landing/src/data/site.ts | 44 + ...jetbrains-mono-latin-ext-wght-normal.woff2 | Bin 0 -> 15196 bytes .../jetbrains-mono-latin-wght-normal.woff2 | Bin 0 -> 40404 bytes .../mona-sans-latin-ext-standard-normal.woff2 | Bin 0 -> 32800 bytes .../mona-sans-latin-standard-normal.woff2 | Bin 0 -> 98124 bytes landing/src/layouts/Base.astro | 138 + landing/src/pages/404.astro | 40 + landing/src/pages/index.astro | 22 + landing/src/styles/base.css | 201 + landing/src/styles/chrome.css | 170 + landing/src/styles/hero.css | 165 + landing/src/styles/sections.css | 236 + landing/src/styles/start.css | 98 + landing/src/styles/surfaces.css | 98 + landing/src/styles/typography.css | 82 + landing/tsconfig.json | 5 + landing/wrangler.jsonc | 12 + package.json | 22 + playground/README.md | 80 + playground/index.html | 21 + playground/package.json | 37 + playground/playwright.config.ts | 22 + playground/public/_headers | 7 + playground/src/assets/eshkra-mark.png | Bin 0 -> 7179 bytes playground/src/env.d.ts | 1 + playground/src/examples/classifier.py | 27 + playground/src/examples/errors.py | 36 + playground/src/examples/pipeline.py | 15 + playground/src/examples/regressor.py | 24 + playground/src/examples/ruff.toml | 9 + playground/src/examples/samples.py | 20 + playground/src/icons.ts | 27 + playground/src/inspector/dom.ts | 87 + playground/src/inspector/overview.ts | 83 + playground/src/inspector/panel.ts | 151 + playground/src/inspector/schema.ts | 48 + playground/src/inspector/structure.ts | 108 + playground/src/links.ts | 9 + playground/src/main.ts | 30 + playground/src/menu.ts | 102 + playground/src/runtime/client.ts | 179 + playground/src/runtime/harness.py | 174 + playground/src/runtime/protocol.ts | 65 + playground/src/runtime/session.ts | 100 + playground/src/runtime/sources.ts | 15 + playground/src/runtime/versions.ts | 40 + playground/src/runtime/worker.ts | 67 + playground/src/settings.ts | 119 + playground/src/shell.ts | 27 + playground/src/styles/base.css | 85 + playground/src/styles/controls.css | 54 + playground/src/styles/editor.css | 37 + playground/src/styles/header.css | 80 + playground/src/styles/index.css | 12 + playground/src/styles/inspector.css | 226 + playground/src/styles/menu.css | 141 + playground/src/styles/output.css | 23 + playground/src/styles/recipes.css | 23 + playground/src/styles/themes.css | 154 + playground/src/styles/tokens.css | 46 + playground/src/styles/viewport.css | 51 + playground/src/styles/workspace.css | 210 + playground/src/viewport.ts | 53 + playground/src/workspace/editor.ts | 69 + playground/src/workspace/examples.ts | 28 + playground/src/workspace/highlight.ts | 12 + playground/src/workspace/mount.ts | 215 + playground/src/workspace/output.ts | 92 + playground/src/workspace/split.ts | 90 + playground/src/workspace/state.ts | 50 + playground/src/workspace/status.ts | 15 + playground/src/workspace/viewer.ts | 29 + playground/tests/e2e/playground.spec.ts | 100 + playground/tests/runtime/examples.test.ts | 151 + playground/tests/runtime/load.ts | 40 + playground/tests/unit/client.test.ts | 203 + playground/tests/unit/config.test.ts | 64 + playground/tests/unit/settings.test.ts | 104 + playground/tsconfig.json | 19 + playground/vite.config.ts | 11 + playground/wrangler.jsonc | 12 + pnpm-lock.yaml | 6106 +++++++++++++++++ pnpm-workspace.yaml | 17 + pyproject.toml | 9 +- scripts/package_smoke.py | 2 +- scripts/publish_check.py | 37 +- src/mlbundle/_schema_inference.py | 28 - src/mlbundle/schema.py | 15 - src/mlbundle/training.py | 7 +- tests/test_publish.py | 24 +- tests/test_readme.py | 34 +- tests/test_series_schema.py | 37 +- uv.lock | 14 +- 186 files changed, 16566 insertions(+), 743 deletions(-) create mode 100644 .github/workflows/deploy-sites.yml create mode 100644 .github/workflows/sites.yml create mode 100644 brand/README.md create mode 100644 brand/mlbundle.png create mode 100644 brand/mona-sans-LICENSE.txt create mode 100644 brand/wordmark-dark.svg create mode 100644 brand/wordmark-light.svg create mode 100644 docs/.gitignore create mode 100644 docs/README.md create mode 100644 docs/astro.config.ts create mode 100644 docs/deployment.md create mode 100644 docs/package.json create mode 100644 docs/src/components/BrandHead.astro create mode 100644 docs/src/components/BrandTitle.astro create mode 100644 docs/src/components/Footer.astro create mode 100644 docs/src/components/SocialIcons.astro create mode 100644 docs/src/components/SpecPage.astro create mode 100644 docs/src/content.config.ts create mode 100644 docs/src/content/docs/adapters/catboost.md create mode 100644 docs/src/content/docs/adapters/onnx.md create mode 100644 docs/src/content/docs/adapters/overview.md create mode 100644 docs/src/content/docs/adapters/pipelines.md create mode 100644 docs/src/content/docs/adapters/sklearn.md create mode 100644 docs/src/content/docs/adapters/xgboost.md create mode 100644 docs/src/content/docs/getting-started/installation.md create mode 100644 docs/src/content/docs/getting-started/quick-start.md create mode 100644 docs/src/content/docs/guides/cli.md create mode 100644 docs/src/content/docs/guides/export.md create mode 100644 docs/src/content/docs/guides/inspect-and-load.md create mode 100644 docs/src/content/docs/guides/records.md create mode 100644 docs/src/content/docs/guides/schema-and-samples.md create mode 100644 docs/src/content/docs/guides/trust.md create mode 100644 docs/src/content/docs/index.md create mode 100644 docs/src/content/docs/reference/api.md create mode 100644 docs/src/content/docs/reference/development.md create mode 100644 docs/src/content/docs/reference/errors.md create mode 100644 docs/src/content/docs/reference/limits.md create mode 100644 docs/src/fonts/jetbrains-mono-LICENSE.txt create mode 100644 docs/src/fonts/jetbrains-mono-latin-ext-wght-normal.woff2 create mode 100644 docs/src/fonts/jetbrains-mono-latin-wght-normal.woff2 create mode 100644 docs/src/fonts/mona-sans-LICENSE.txt create mode 100644 docs/src/fonts/mona-sans-latin-ext-standard-normal.woff2 create mode 100644 docs/src/fonts/mona-sans-latin-standard-normal.woff2 create mode 100644 docs/src/navigation.ts create mode 100644 docs/src/pages/format/[...part].astro create mode 100644 docs/src/pages/security.astro create mode 100644 docs/src/sites.ts create mode 100644 docs/src/spec.test.ts create mode 100644 docs/src/spec.ts create mode 100644 docs/src/styles/content.css create mode 100644 docs/src/styles/home.css create mode 100644 docs/src/styles/theme.css create mode 100644 docs/src/styles/typography.css create mode 100644 docs/tsconfig.json create mode 100644 docs/wrangler.jsonc create mode 100644 landing/.gitignore create mode 100644 landing/.prettierignore create mode 100644 landing/.prettierrc.json create mode 100644 landing/README.md create mode 100644 landing/astro.config.ts create mode 100644 landing/examples/train.py create mode 100644 landing/package.json create mode 100644 landing/public/_headers create mode 100644 landing/public/licenses/jetbrains-mono.txt create mode 100644 landing/public/licenses/mona-sans.txt create mode 100644 landing/public/robots.txt create mode 100644 landing/public/sitemap.xml create mode 100644 landing/ruff.toml create mode 100644 landing/scripts/example.py create mode 100644 landing/src/components/BundleCard.astro create mode 100644 landing/src/components/Formats.astro create mode 100644 landing/src/components/Hero.astro create mode 100644 landing/src/components/Icon.astro create mode 100644 landing/src/components/Inside.astro create mode 100644 landing/src/components/Schema.astro create mode 100644 landing/src/components/Start.astro create mode 100644 landing/src/components/Terminal.astro create mode 100644 landing/src/components/Verify.astro create mode 100644 landing/src/components/Wordmark.astro create mode 100644 landing/src/data/example.json create mode 100644 landing/src/data/example.ts create mode 100644 landing/src/data/site.ts create mode 100644 landing/src/fonts/jetbrains-mono-latin-ext-wght-normal.woff2 create mode 100644 landing/src/fonts/jetbrains-mono-latin-wght-normal.woff2 create mode 100644 landing/src/fonts/mona-sans-latin-ext-standard-normal.woff2 create mode 100644 landing/src/fonts/mona-sans-latin-standard-normal.woff2 create mode 100644 landing/src/layouts/Base.astro create mode 100644 landing/src/pages/404.astro create mode 100644 landing/src/pages/index.astro create mode 100644 landing/src/styles/base.css create mode 100644 landing/src/styles/chrome.css create mode 100644 landing/src/styles/hero.css create mode 100644 landing/src/styles/sections.css create mode 100644 landing/src/styles/start.css create mode 100644 landing/src/styles/surfaces.css create mode 100644 landing/src/styles/typography.css create mode 100644 landing/tsconfig.json create mode 100644 landing/wrangler.jsonc create mode 100644 package.json create mode 100644 playground/README.md create mode 100644 playground/index.html create mode 100644 playground/package.json create mode 100644 playground/playwright.config.ts create mode 100644 playground/public/_headers create mode 100644 playground/src/assets/eshkra-mark.png create mode 100644 playground/src/env.d.ts create mode 100644 playground/src/examples/classifier.py create mode 100644 playground/src/examples/errors.py create mode 100644 playground/src/examples/pipeline.py create mode 100644 playground/src/examples/regressor.py create mode 100644 playground/src/examples/ruff.toml create mode 100644 playground/src/examples/samples.py create mode 100644 playground/src/icons.ts create mode 100644 playground/src/inspector/dom.ts create mode 100644 playground/src/inspector/overview.ts create mode 100644 playground/src/inspector/panel.ts create mode 100644 playground/src/inspector/schema.ts create mode 100644 playground/src/inspector/structure.ts create mode 100644 playground/src/links.ts create mode 100644 playground/src/main.ts create mode 100644 playground/src/menu.ts create mode 100644 playground/src/runtime/client.ts create mode 100644 playground/src/runtime/harness.py create mode 100644 playground/src/runtime/protocol.ts create mode 100644 playground/src/runtime/session.ts create mode 100644 playground/src/runtime/sources.ts create mode 100644 playground/src/runtime/versions.ts create mode 100644 playground/src/runtime/worker.ts create mode 100644 playground/src/settings.ts create mode 100644 playground/src/shell.ts create mode 100644 playground/src/styles/base.css create mode 100644 playground/src/styles/controls.css create mode 100644 playground/src/styles/editor.css create mode 100644 playground/src/styles/header.css create mode 100644 playground/src/styles/index.css create mode 100644 playground/src/styles/inspector.css create mode 100644 playground/src/styles/menu.css create mode 100644 playground/src/styles/output.css create mode 100644 playground/src/styles/recipes.css create mode 100644 playground/src/styles/themes.css create mode 100644 playground/src/styles/tokens.css create mode 100644 playground/src/styles/viewport.css create mode 100644 playground/src/styles/workspace.css create mode 100644 playground/src/viewport.ts create mode 100644 playground/src/workspace/editor.ts create mode 100644 playground/src/workspace/examples.ts create mode 100644 playground/src/workspace/highlight.ts create mode 100644 playground/src/workspace/mount.ts create mode 100644 playground/src/workspace/output.ts create mode 100644 playground/src/workspace/split.ts create mode 100644 playground/src/workspace/state.ts create mode 100644 playground/src/workspace/status.ts create mode 100644 playground/src/workspace/viewer.ts create mode 100644 playground/tests/e2e/playground.spec.ts create mode 100644 playground/tests/runtime/examples.test.ts create mode 100644 playground/tests/runtime/load.ts create mode 100644 playground/tests/unit/client.test.ts create mode 100644 playground/tests/unit/config.test.ts create mode 100644 playground/tests/unit/settings.test.ts create mode 100644 playground/tsconfig.json create mode 100644 playground/vite.config.ts create mode 100644 playground/wrangler.jsonc create mode 100644 pnpm-lock.yaml create mode 100644 pnpm-workspace.yaml delete mode 100644 src/mlbundle/_schema_inference.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index db55b91..9b3e043 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -5,6 +5,7 @@ on: branches: [main, develop] pull_request: branches: [main, develop] + workflow_dispatch: workflow_call: inputs: prepare-publish: @@ -16,6 +17,23 @@ permissions: contents: read jobs: + sites: + name: Frontend checks + uses: ./.github/workflows/sites.yml + + deploy-sites: + name: Deploy websites + needs: [build, base-install, lint, types, tests, sites] + if: >- + github.repository == 'UlloaSP/mlbundle' && + github.ref == 'refs/heads/main' && + !inputs.prepare-publish && + (github.event_name == 'push' || github.event_name == 'workflow_dispatch') + uses: ./.github/workflows/deploy-sites.yml + secrets: + CLOUDFLARE_API_TOKEN: ${{ secrets.CLOUDFLARE_API_TOKEN }} + CLOUDFLARE_ACCOUNT_ID: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }} + build: name: Build distributions runs-on: ubuntu-latest diff --git a/.github/workflows/deploy-sites.yml b/.github/workflows/deploy-sites.yml new file mode 100644 index 0000000..aed28bd --- /dev/null +++ b/.github/workflows/deploy-sites.yml @@ -0,0 +1,71 @@ +name: Deploy websites + +on: + workflow_call: + secrets: + CLOUDFLARE_API_TOKEN: + required: true + CLOUDFLARE_ACCOUNT_ID: + required: true + +permissions: + contents: read + +jobs: + deploy: + name: Deploy ${{ matrix.site }} + if: github.repository == 'UlloaSP/mlbundle' && github.ref == 'refs/heads/main' + runs-on: ubuntu-latest + timeout-minutes: 15 + strategy: + fail-fast: false + matrix: + include: + - site: landing + url: https://mlbundle.eshkra.dev + title: MLbundle + - site: docs + url: https://docs.mlbundle.eshkra.dev + title: mlbundle documentation + - site: playground + url: https://playground.mlbundle.eshkra.dev + title: MLbundle Playground + environment: + name: ${{ matrix.site }} + url: ${{ matrix.url }} + concurrency: + group: mlbundle-${{ matrix.site }}-production + cancel-in-progress: false + steps: + - uses: actions/checkout@v4 + + - name: Setup Vite+ + uses: voidzero-dev/setup-vp@v1.21.2 + with: + node-version: '24.14.1' + cache: true + + - name: Install locked deployment tooling + run: vp install --frozen-lockfile + + - name: Download tested site + uses: actions/download-artifact@v4 + with: + name: ${{ matrix.site }}-build + path: ${{ matrix.site }}/dist/ + + - name: Deploy static assets and custom domain + run: vp exec wrangler deploy --config "$SITE/wrangler.jsonc" + env: + SITE: ${{ matrix.site }} + CLOUDFLARE_API_TOKEN: ${{ secrets.CLOUDFLARE_API_TOKEN }} + CLOUDFLARE_ACCOUNT_ID: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }} + + - name: Check the deployed homepage + env: + SITE_URL: ${{ matrix.url }} + EXPECTED_TITLE: ${{ matrix.title }} + run: | + curl --fail --silent --show-error --location --retry 10 --retry-delay 15 \ + --retry-all-errors --max-time 20 "$SITE_URL/" -o "$RUNNER_TEMP/site.html" + grep -Fq "$EXPECTED_TITLE" "$RUNNER_TEMP/site.html" diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 054c4a0..cf820c1 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -1,7 +1,9 @@ name: Publish to PyPI on: - workflow_dispatch: + push: + branches: [main] + paths: [pyproject.toml] permissions: contents: read @@ -11,26 +13,45 @@ concurrency: cancel-in-progress: false jobs: + preflight: + name: Check version change + runs-on: ubuntu-latest + outputs: + publish: ${{ steps.check.outputs.publish }} + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: Check version change and PyPI availability + id: check + env: + PREVIOUS_REF: ${{ github.event.before }} + run: python scripts/publish_check.py --previous-ref "$PREVIOUS_REF" + build: + needs: preflight + if: needs.preflight.outputs.publish == 'true' uses: ./.github/workflows/ci.yml with: prepare-publish: true publish: - needs: build + needs: [preflight, build] + if: needs.preflight.outputs.publish == 'true' && needs.build.result == 'success' runs-on: ubuntu-latest environment: name: pypi_env url: https://pypi.org/p/mlbundle - permissions: - id-token: write steps: - name: Download CI-validated distributions uses: actions/download-artifact@v4 with: name: distributions path: dist - - name: Publish to PyPI with Trusted Publishing + - name: Publish to PyPI with the project token uses: pypa/gh-action-pypi-publish@release/v1 with: packages-dir: dist/ + user: __token__ + password: ${{ secrets.MLBUNDLE_API_TOKEN }} diff --git a/.github/workflows/sites.yml b/.github/workflows/sites.yml new file mode 100644 index 0000000..5abaaad --- /dev/null +++ b/.github/workflows/sites.yml @@ -0,0 +1,77 @@ +name: Check and build websites + +on: + workflow_call: + +permissions: + contents: read + +jobs: + check-build: + name: Frontend lint, types, tests and builds + runs-on: ubuntu-latest + timeout-minutes: 30 + steps: + - uses: actions/checkout@v4 + + - name: Setup Vite+ + uses: voidzero-dev/setup-vp@v1.21.2 + with: + node-version: '24.14.1' + cache: true + + - name: Install locked dependencies + run: vp install --frozen-lockfile + + - name: Check lint, Astro formatting, types and unit tests + run: vp run check + + - name: Test the Python browser runtime and all examples + run: vp run mlbundle-playground#test:runtime + + - name: Build all three sites + run: vp run build + + - name: Validate Cloudflare deployment configuration + run: vp run check:deploy + + - name: Install Chromium + working-directory: playground + run: vp exec playwright install --with-deps chromium + + - name: Test the built playground on desktop and mobile + run: vp run mlbundle-playground#test:e2e:built + + - name: Keep browser failure evidence + if: failure() + uses: actions/upload-artifact@v4 + with: + name: playground-test-results + path: playground/test-results/ + if-no-files-found: ignore + retention-days: 7 + + # Only tested builds become deployable artifacts. Deployment never rebuilds them. + - name: Upload landing + uses: actions/upload-artifact@v4 + with: + name: landing-build + path: landing/dist/ + if-no-files-found: error + retention-days: 7 + + - name: Upload documentation + uses: actions/upload-artifact@v4 + with: + name: docs-build + path: docs/dist/ + if-no-files-found: error + retention-days: 7 + + - name: Upload playground + uses: actions/upload-artifact@v4 + with: + name: playground-build + path: playground/dist/ + if-no-files-found: error + retention-days: 7 diff --git a/.gitignore b/.gitignore index 486d3b8..105425f 100644 --- a/.gitignore +++ b/.gitignore @@ -16,6 +16,12 @@ __pycache__/ .cache/ .tox/ .nox/ +node_modules/ +.astro/ +.vite/ +.wrangler/ +playwright-report/ +test-results/ # Build output build/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 2bf37d3..16b35fa 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -5,8 +5,10 @@ repos: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml + exclude: ^pnpm-lock\.yaml$ - id: check-toml - id: check-added-large-files + exclude: ^brand/mlbundle\.png$ - id: check-merge-conflict - repo: https://github.com/astral-sh/ruff-pre-commit @@ -20,3 +22,4 @@ repos: rev: v1.1.414 hooks: - id: pyright + files: ^src/ diff --git a/AGENTS.md b/AGENTS.md index 427cbfe..adee395 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,149 +1,179 @@ -# mlbundle - -mlbundle is the training-side companion to MLSuite: a Python library for putting -a trained model, a rich manifest, a locally inferred schema and model structure -in one compressed `.mlbundle` file. It owns both writing and validated reading -so that the format is defined once. - -The pre-release v0.1 library implements strict format 1 manifests, secure container -I/O, inspect/export/load, sklearn/XGBoost/CatBoost/ONNX adapters, local schema, training -dtypes, explicit samples and prediction-state verification. The CLI provides -metadata-only `inspect` and full `verify`. Format 1 is not yet frozen. -Implementation claims require code and test proof. `docs/format.md` owns the wire contract. - -## What makes mlbundle special? - -### 1. Reproducibility across the training/runtime boundary - -MLSuite turns trained models into reviewable applications; it does not train them. -Its runtime accepts only `.mlbundle` uploads. Preserve the identity and meaning -of the model, schema, features, classes and outputs across this boundary. - -### 2. Metadata is a claim, even when it looks useful - -The author of a bundle may be an attacker. Everything derived from a model, -including hyperparameters, feature/class/output counts, coefficients, feature -importances and tree structure, is a claim. Writer and reader must use the same -extraction code. The reader must recompute these claims from the loaded model -and reject mismatches. A reviewer must never see a tree that differs from the -model used for prediction. - -### 3. Training data stays on the training machine - -mlbundle depends on mlschema to call `mlschema.infer_schema` locally; mlschema -does not depend on or know mlbundle. The bundle carries the resulting fields -and report definitions. Do not include the training DataFrame. Include sample -rows only when explicitly requested: samples are real user data. - -### 4. Native formats with a narrow support boundary - -Version 0.1 supports four libraries: scikit-learn via `skops.io.dumps/loads`, -XGBoost via native in-memory UBJSON, CatBoost via CBM, and the user's ONNX -file unchanged. Do not convert models. Adapters import their library lazily; -their dependencies belong in the optional extras `mlbundle[sklearn]`, -`mlbundle[xgboost]`, `mlbundle[catboost]` and `mlbundle[onnx]`. - -The design takes ideas from MLflow's versioned manifest and native model -storage, not its code. MLflow is Apache-2.0; mlbundle is MIT. - -## A note from the maintainer - -Pablo Ulloa Santín prefers ambitious ML workflows and simple systems. Find the -owner of a rule, implement it once, and make the behavior obvious. Measure -twice, cut once, and resist scope creep. The smallest complete change must -still preserve validation, security and reproducibility. - -## A small glossary - -- **bundle** — the zip container with the `.mlbundle` extension. -- **manifest** — the versioned JSON record of identity, model metadata, - environment, member hashes and sizes, and user metadata. -- **member** — a named file inside the zip; only a closed set is permitted. -- **adapter** — mlbundle-owned code for one supported model library, including - serialization, loading and shared extraction of model-derived claims. -- **claim** — information asserted by the bundle's author; model-derived claims - require comparison with the loaded model using canonical JSON byte equality. -- **predictor** — the adapter's uniform probability/regression interface; the - native model remains available separately for runtime explanations. -- **schema** — locally inferred mlschema fields plus report definitions. -- **structure** — model internals for visualisation, tied to the actual model. -- **trusted types** — the mlbundle-owned allowlist used for skops loading. -- **inspect** — metadata-only reading that never deserializes the model. -- **runtime** — the consumer that validates a bundle before using its model. - -## The three ways to hurt yourself - -1. **Trusting the manifest.** Checksums and plausible metadata do not make an - attacker honest. Recompute model claims and reject disagreement. -2. **Opening a convenience loading path.** Pickle fallbacks, dynamic imports and - author-supplied trusted types undo the security boundary. Reject unsupported - models on the training machine instead. -3. **Defining the format twice.** Separate writer/reader extraction or schema - rules drift. Share the owning implementation and change every affected path. - -## Hit every affected layer - -- **Format.** Manifest validation, allowed members, version handling and - `docs/format.md` must agree. Reject unknown versions instead of guessing. -- **Writer.** Compute the schema locally, record the environment and fail early - for unsupported model types. Samples require explicit user intent. -- **Reader.** Validate the archive and manifest, verify hashes, load only through - closed adapters, and recompute model-derived claims. -- **Structure.** Tree arrays, ensemble tree lists, XGBoost tree dumps, linear - coefficients and feature importances must describe the loaded model. -- **Packaging.** Keep model libraries optional, imports lazy and the base - package usable without an adapter's dependencies. -- **Tests.** Cover successful round trips and every rejection path together in - the feature's test file. - -## Guardrails - -### Reader security rules +# MLbundle + +mlbundle is a Python library for packaging a trained model, a manifest, a locally +inferred schema and model structure in one compressed `.mlbundle` file. + +It owns export, metadata-only inspection and validated loading. It is the +training-side companion to MLSuite, whose runtime accepts `.mlbundle` uploads. +MLSuite does not train models. The package version is 0.2.0; format 1 is not frozen. +Implementation claims require code and test proof. + +## What must remain true + +### 1. The format is one contract + +`docs/format.md` owns the wire contract and links its normative sections. +Writer, reader, adapters, schemas and visualizations must agree with it. +Reject unknown format versions; change public contracts and versions deliberately. + +### 2. Metadata is a claim + +The author of a bundle may be an attacker. Model parameters, feature/class/output +counts, coefficients, importances and tree structure are claims, even when useful. +Writer and reader use the same extraction code. Load recomputes model-derived +claims and structure and compares canonical JSON bytes, rejecting disagreement. +A reviewer must see the model used for prediction. + +### 3. Training data stays local + +mlbundle calls `mlschema.infer_schema` locally. mlschema does not depend on or know +mlbundle. Carry its fields and report definitions, never the training DataFrame. +Include real rows only through explicit `sample_rows=N`; samples are user data. +Names, categories, learned models and structure can still reveal training information. + +### 4. Native models cross a closed boundary + +Support scikit-learn through skops, XGBoost through in-memory UBJSON, CatBoost +through CBM and existing ONNX bytes unchanged. Do not convert models. +Adapters import their libraries lazily; model dependencies belong in optional +`sklearn`, `xgboost`, `catboost` and `onnx` extras. + +### 5. Inspection and verification mean different things + +`inspect` validates container, manifest, integrity and schema/sample declarations +without importing model libraries or deserializing models. It cannot attest +model-derived claims. `load` additionally verifies native claims and structure. +The CLI exposes these as `inspect` and `verify`. Keep this distinction visible +in APIs, docs and the playground. + +## Maintainer intent + +Pablo Ulloa Santín favors ambitious ML workflows built from simple systems. +Find the owner of a rule, implement it once and make its behavior obvious. +The smallest complete change preserves validation, security and reproducibility +through every affected path. + +Remove complexity whose constraint no longer exists. Keep compatibility only +when required by the public contract or the task. Avoid speculative flags, +dead stubs and undocumented fallback behavior. + +## Vocabulary + +- **bundle**: the ZIP container with the `.mlbundle` extension +- **manifest**: versioned identity, model metadata, environment, integrity records and user metadata +- **member**: a named file from the container's closed allowed set +- **adapter**: owned recognition, native I/O, shared extraction and prediction for one library +- **claim**: information asserted by the author; model-derived claims require recomputation +- **schema**: locally inferred mlschema fields and report definitions +- **structure**: model internals for visualization, bound to the loaded model +- **predictor**: uniform probability/regression interface, separate from the native model +- **trusted types**: mlbundle-owned allowlists for skops loading +- **runtime**: the consumer that validates a bundle before using its model + +## Invariants + +- Source, test and documentation files stay at or below 300 lines. Split by responsibility. +- Pydantic v2 is the sole core runtime dependency. New dependencies need a concrete reason; model libraries remain extras. +- Keep `uv_build` as the Python build backend. +- Avoid Python 3.14-only syntax so widening support can remain a metadata change. +- Preserve model, feature, class, output and schema identity across export and load. +- Safety ceilings may only be lowered by callers. +- Tests for a feature keep successful round trips and rejection cases together in one file. +- Verification claims describe commands actually run and their real results. +- `C:\software\mlsuite`, `C:\software\mlschema`, `C:\software\mlform` and `C:\software\eshkra` are read-only references. Never modify, commit or run write commands in them. +- sklearn-to-ONNX conversion, per-model environments, bundle signing and additional model libraries (including LightGBM and PyTorch) are outside scope. + +## Reader security + +These rules apply to every loading and inspection path, including examples and UI: - Never use pickle, joblib or cloudpickle, even as an option or fallback. -- Never import or instantiate from strings in the manifest. The class to - instantiate must come from a closed list inside mlbundle. -- The skops trusted lists live in mlbundle; never read them from a bundle or add XGBoost/CatBoost types. Tree, optimizers, scalar dtypes and closed functions each admit only their owning loaders; see `docs/format-state.md`. -- Export must fail on the user's machine when the model contains custom types, - listing unsupported types from `skops.io.get_untrusted_types`. Do not defer - discovery to upload time or offer to trust those types. -- Read the zip in memory by allowed member names. Never extract it to disk. - Reject unknown or duplicate members, paths with separators or `..`, symlinks - and encrypted entries. Enforce limits on member count, compressed size, - uncompressed size and compression ratio before allocating or loading content. -- Verify each member's SHA-256 against the manifest. Hashes detect corruption, - not deliberate tampering; signing is out of scope. -- Validate the manifest strictly and reject an unknown format version. -- Provide a metadata-only `inspect` read that does not deserialize the model. -- Recompute all model-derived claims with the writer's extraction code and - reject a bundle when they differ from the loaded model. - -### Repository rules - -- No source, test or documentation file may exceed 300 lines. Split by responsibility before the limit. -- Add no runtime dependency without a concrete reason. The sole core dependency - is Pydantic v2 for strict manifest validation; model libraries belong in extras. -- Keep a feature's tests in one file covering its success case and every error - case. Test observable behavior and supported contracts. -- Avoid Python 3.14-only syntax; widening the Python range should be a metadata - change rather than a source rewrite. -- Preserve public contracts and change format versions deliberately. Leave no - dead stubs, speculative flags or undocumented fallback behavior. -- Use Conventional Commits (`chore:`, `docs:`, `ci:`, `feat:`, `fix:`, `test:`). -- Never add `Co-Authored-By`, AI attribution or "Generated with" lines to commits - or PR text. -- `C:\software\mlsuite` and `C:\software\mlschema` are read-only references. - Never modify, commit or run write commands inside either checkout. -- Publish only via manual `publish.yml` from `main`, through `pypi_env` with owner approval, on the owner's explicit request for that specific version. - Agents never run or approve it on their own initiative or add publish paths/tokens/secrets. No GitHub releases/version tags; "tags" means GitHub repository topics. -- sklearn-to-ONNX conversion, per-model environments, bundle signing and additional - libraries (including LightGBM and PyTorch) are outside the project scope. -- Never claim a verification that was not run; report its real output or blocker. - -## Development - -Work from the repository root using `uv` on Python 3.14. The optional `schema` -extra admits mlschema >=0.2.1,<0.3 on 3.14 only. Keep `uv_build` as the build backend. +- Instantiate only from mlbundle's closed class lists. Never import or instantiate from manifest strings. +- Own skops trusted lists in mlbundle; never accept them from a bundle or add XGBoost/CatBoost types. Trees, optimizers, scalar dtypes and closed functions admit only their owning loaders. +- Reject custom types at export on the training machine. List unsupported types from `skops.io.get_untrusted_types`; never offer to trust them or defer rejection to upload. +- Read ZIP members in memory by allowed names. Never extract the archive to disk. +- Reject unknown/duplicate members, separators or `..` in names, symlinks and encrypted entries. +- Enforce member count, compressed size, uncompressed size and compression ratio before allocation or loading. +- Validate the manifest strictly and verify every non-manifest member's SHA-256 and size. Hashes detect corruption, not authenticity or deliberate tampering. +- Recompute model-derived claims and structure using the writer's extraction code before returning a loaded model. +- Keep errors bounded. Record-input errors name escaped keys without echoing values. + +Read `SECURITY.md` before changing trust boundaries. Read `docs/format-container.md` +before changing archive parsing or limits, and `docs/format-state.md` before +changing skops trust, fitted state or supported types. + +## How MLbundle fits together + +1. A closed adapter recognizes the model and prepares it without mutating the user's object. +2. Shared extraction derives model claims and structure from native state. +3. Native serialization and reload prove those claims before export publishes the bundle. +4. Local mlschema inference supplies fields/reports; shared code resolves features, targets and environment. +5. The writer computes integrity records, validates through the reader and writes a deterministic ZIP. +6. Inspect validates bounded container/JSON data and declarations without adapter resolution. +7. Load uses the closed adapter and compares recomputed claims and structure. +8. The predictor and record APIs expose validated inputs and output-major report results. + +The allowed members are manifest, required schema, one native member (or a declared +skops + UBJSON/CBM pair), extractable structure and explicit samples: at most six. +Publication uses atomic hard links or replacement and requires explicit overwrite. +The exclusive-create fallback removes partial destinations on failure but exposes +the file during copying. Preserve that behavior and document its limits honestly. + +## Ownership and contract references + +Use the entry points and linked contracts to find the owning implementation: + +- **Public I/O:** `model_io.py`, `inspection.py`, `results.py` and `cli.py`; `__init__.py` owns public exports. +- **Container:** `container.py`, `writing.py`, `_container_rules.py`, `_zip_directory.py`, `_zip_members.py`, `limits.py` and `integrity.py`. +- **Declarations:** `manifest.py`, `canonical.py`, `declarations.py`, `environment.py`, `training.py`, `schema.py` and `samples.py`. +- **Adapters:** `adapters.py` and `registry.py` define closed lazy resolution; `_sklearn/`, `_xgboost/`, `_catboost/`, `_onnx/` and `_composite/` own native behavior. +- **Claims and predictions:** `claims.py`, `records.py`, `_prediction_input.py`, `_prediction_decisions.py` and `_display.py`. +- **Websites:** `landing/`, `docs/` and `playground/`; `brand/` owns the shared identity sourced from eshkra. + +Paths above are relative to `src/mlbundle/` unless they name a root directory. +Use package configuration and source for detailed inventories and command options. + +When changing schema, mappings or samples, read `docs/format-schema.md`: + +- Every export needs the schema extra, including default inference from one float-1.0 row. +- Schema v2 records source and separator (default `__`). Real frames exclude every declared target and record training dtypes; default dtypes are null. +- Native model names map by name; other origins map by position, with generated names starting at `feature_0`. +- Unique target names map by output position. Reports use string labels; manifest labels retain their types. +- MLschema owns series columns and explicit conversion to positional mappings. Export/inspect/load share declaration validation without reader-side mlschema. + +When changing native I/O or extraction, read `docs/format-adapters.md` and the +adapter-specific contracts it links: + +- `prepare_export` accepts strict `ExportDeclarations` and leaves the original model unchanged. Missing native names are null; shared code owns declared/generated names and environment. +- Null native classifier labels permit count-checked declarations. Sklearn/CatBoost own `classes_` and reject `class_labels=`; XGBoost binds supplied output-major labels to a native copy and otherwise uses encoded indices. +- XGBoost supports exact single-target tree wrappers with closed objectives. CatBoost supports exact numeric single-target symmetric models with complete native state/structure and in-memory CBM loading. +- Read `docs/format-pipeline.md` for composites. Exact outer sklearn Pipelines ending in XGBoost/CatBoost use checked skops/native components; other native placements remain rejected. Composite trees address transformed inputs; all-sklearn Pipeline structure stays null. +- Read `docs/format-onnx.md` before changing ONNX. Preserve path/bytes unchanged, serialize ModelProto once and validate once per export/load with bounded scalar scans before CPU sessions. Reject external tensors, custom domains/functions and runtime/training configuration. Verify signatures, metadata/environment claims and native labels; structure stays null. +- Native errors become bounded `MLBundleError`s. Version hints diagnose failed manifests/skops loads without adding compatibility gates. + +When changing record prediction, preserve shared schema-key lookup, bounded +numeric-string/bool conversion and schema-permitted None-to-NaN handling. +Never iterate extra keys. Frozen output-major results use the native class +decision's report label, never one inferred from probabilities. + +## Check every affected path + +Apply the entries relevant to the change: + +- **Format:** versions, manifest validation, allowed members and normative docs agree. +- **Writer:** unsupported models fail early; local inference, environment and explicit samples remain correct. +- **Reader:** archive bounds, hashes, closed loading and recomputed claims remain enforced. +- **Structure:** visualized trees, coefficients and importances describe the loaded model. +- **Schema:** fields, reports, names, dtypes, mappings and samples agree across export/inspect/load. +- **Packaging:** base installs work without adapter libraries; imports remain lazy. +- **Websites:** examples use actual APIs, inspection claims are accurate, controls work by keyboard and layouts remain usable on mobile. +- **Tests:** success and affected rejection paths exercise observable contracts. + +A change is incomplete when it works only through the path used by its first test. + +## Development and testing + +Work from the repository root with `uv` on Python 3.14: ```shell uv python install 3.14 @@ -151,14 +181,16 @@ uv sync --locked --all-extras uv run pre-commit install ``` -After changing dependency metadata, use `uv sync` to regenerate `uv.lock` and -commit the lockfile. CI and manual `publish.yml` use locked dev/all extras on 3.14. -Default dev installs `mlbundle[all]`; base users can omit it. Publishing reuses CI. -Ruff handles lint and formatting; Pyright uses the family's basic mode. +The schema extra admits mlschema `>=0.3.0,<0.4` on Python 3.14 only. Default dev +installs include `mlbundle[all]`; base users can omit them. Dependency metadata +changes require `uv sync` to regenerate `uv.lock` and inclusion of the updated +lockfile in the change. CI and publishing use locked dev/all extras on 3.14. -## Verifying +Start with the smallest proof of observable behavior. Cover success and affected +errors together; use regression tests for bugs and integration tests for shared +boundaries. Use browser checks when rendering, focus or keyboard behavior matters. -Run these commands from the repository root: +Before handing off Python source or package changes, run: ```shell uv run ruff check . @@ -168,133 +200,49 @@ uv run pytest uv build ``` -The Ruff commands also work without the final `.` because the default is the -current directory: `uv run ruff check` and `uv run ruff format --check`. -`uv run pytest` runs `tests/`, measures branch coverage and enforces the family's -80% minimum, writing terminal and XML reports. Tests use the installed package; -its version comes from package metadata. CI checks lint/types and the built wheel -on Ubuntu/Windows; manual `publish.yml` requires that CI. Build a wheel and sdist. +Ruff owns lint/formatting; Pyright uses basic mode. Tests use the installed package, +obtain its version from metadata and enforce 80% branch-inclusive coverage with +terminal/XML reports. Build both wheel and sdist. CI tests the built wheel on +Ubuntu/Windows, including base installs. -Start with the smallest proof of a behavior change, then broaden when needed. -If a check cannot run, report the exact command and blocker. Never claim a test, -environment or behavior was verified when it was not. +Use Vite+ (`vp`) for all three frontend projects: `vp install`, `vp run check` +and `vp run build` at the root, or the site's `package.json` scripts via `vp run`. +Astro commands go through `vp exec`; the playground uses Vite+ directly. +A docs-only edit needs relevant examples/links/build checks, not every +Python gate. If any check cannot run, report its exact command and blocker. ## Documentation -- `README.md` owns purpose, honest implementation status and development setup. -- `SECURITY.md` owns the threat model, guarantees, limits and private reporting. -- `docs/format.md` owns the version 1 wire specification and verification rules. -- Rewrite stale guidance instead of appending competing accounts. +Keep README focused on identity, installation, primary usage, supported surfaces +and links to detailed guides. `SECURITY.md` owns the threat model, guarantees, +limits and private reporting. `docs/format.md` owns the wire specification; +web docs must reuse the normative files instead of maintaining parallel copies. + +Document user-visible contracts and durable cross-module decisions. Rewrite +stale guidance rather than appending a competing account. Keep all agent policy +in this root file; do not add nested AGENTS.md files. -Keep agent policy in this root file. Do not add nested `AGENTS.md` files. +## Git, publishing and work artifacts -## Plans and work artifacts +Preserve unrelated working-tree changes. Do not commit, push, create branches +or open pull requests unless explicitly requested. Use Conventional Commits +(`chore:`, `docs:`, `ci:`, `feat:`, `fix:`, `test:`). +Never add `Co-Authored-By`, AI attribution or "Generated with" lines to commits or PRs. + +PyPI publication is automatic when `project.version` changes in a push to `main`. +`publish.yml` must run the full CI workflow first and publish only that new version +through the `pypi_env` environment, whose branch policy is limited to `main` and +whose `MLBUNDLE_API_TOKEN` secret is project-scoped. Never read or expose secret +values, change versions just to trigger publication, or invoke uploads manually. +No GitHub releases or version tags; "tags" means repository topics. Do not create plan, notes, TODO, lessons, scratch or tech-debt files in the repo. -The conversation, issues, code, tests and final diff are the work record. - -## How it works - -The writer accepts member bytes and manifest parts, computes integrity records, -validates through the reader and produces a deterministic ZIP. Publication uses -atomic hard links or replacement, requiring explicit overwrite. Exclusive-create -fallback removes partial destinations on failure but exposes the file while copying. -Sklearn/XGBoost/CatBoost exports verify shared claims/structure after a native -skops/UBJSON/CBM round trip before publication. Safety ceilings may only be lowered. - -Every export requires the schema extra. mlschema infers from `training_data=` or -one float-1.0 row, recording schema v2 source/separator (default `__`). Real frames -exclude every declared target and record training dtypes; default dtypes are null. -Only explicit `sample_rows=N` includes real rows. Model-origin mappings use names; -others use positions; generated names start at `feature_0`. Unique `target_names` -map by output position. Reports use string labels; manifest labels retain types. -Series pairs are adapted once to MLForm columns until mlschema emits that shape. -Export/inspect/load share schema/report/sample validation without reader-side mlschema. - -XGBoost supports exact single-target tree wrappers with closed objectives. Supplied -output-major labels bind to a native copy via an owned booster attribute and are -verified on load; omitted labels use encoded indices. Sklearn/CatBoost own classes_ -and reject `class_labels=`. CatBoost supports exact numeric single-target symmetric -models, complete native state/structure and in-memory CBM loading. Exact outer -sklearn Pipelines ending in XGBoost/CatBoost use checked skops/native components; -other native placements remain rejected. - -ONNX preserves path/bytes unchanged (ModelProto serialized once), validates once -per export/load with bounded scalar scans, and rejects external tensors, custom -domains/functions and runtime/training configuration before a CPU session. It -verifies signatures, metadata/environment claims and native labels; absent labels -use count-checked declarations. OnnxModel retains bytes/session; structure is null. -Read the normative ONNX section linked from `docs/format.md` for layouts and bounds. - -The closed member set is manifest, required schema, one native member (or declared -skops + UBJSON/CBM pair), required extractable structure, and explicit samples: -at most six members. The manifest records identity/time/writer, model metadata, -ordered features/origin/dtypes, output-major targets/classes, training environment, -member hashes/sizes and user metadata. Model-exposed dtypes are separately verified. -Every declaration is untrusted; hashes do not establish authenticity. - -Inspect validates archive/JSON bounds, manifest, integrity and schema/samples -without model imports/deserialization; it cannot attest model-derived claims. -Load uses closed adapters and shared extraction to recompute claims and structure. -`row_from_record`/`predict_record` share schema-key lookups, bounded numeric-string -and bool conversion, optional None-to-NaN handling and escaped key errors without -values. Extra keys are never iterated. Predictions are frozen output-major report -results, including the native class decision's report label (never inferred from -probabilities). Safe version hints diagnose failed manifests/skops loads without new gates. - -Adapters own recognition, preparation, serialization/loading, claims, structure -and prediction; extraction uses only native state and is shared by export/load. -`prepare_export` receives strict ExportDeclarations and leaves the user's object -unchanged. Missing native names are null; shared code owns declared/generated names -and environment. Null native classifier labels permit count-checked declarations, -including ONNX without byte changes. Native errors become bounded MLBundleErrors. -Keep native behavior in adapters; follow `adapters.py`. - -## Where code lives - -- `src/mlbundle/__init__.py` — public inspect/export/load, contract models, enums, errors and package version. -- `src/mlbundle/cli.py` — argparse inspect/verify console entry with safe bounded display. -- `src/mlbundle/_base.py` and `errors.py` — strict frozen model configuration, string bounds and bounded errors. -- `src/mlbundle/contract.py` — closed identifiers and native member/format pairing. -- `src/mlbundle/limits.py` — shared archive ceilings and specific member caps. -- `src/mlbundle/canonical.py` and `_canonical_work.py` — deterministic JSON and bounded decoding/expansion. -- `src/mlbundle/model_io.py`, `_native_io.py`, `_export_manifest.py` — public I/O, native round trips and manifest construction. -- `src/mlbundle/_composite/`, `_pipeline_contract.py` — strict recipe, separate I/O, closed reconstruction/claims/prediction and input/inner boundaries. Read `docs/format-pipeline.md` when changing composites. -- `src/mlbundle/training.py` — lazy local/default inference and feature origin/dtype resolution. -- `src/mlbundle/schema.py` and `_schema_fields.py` — closed schema/report declarations and reader cross-checks without mlschema. -- `src/mlbundle/_schema_inference.py` — temporary mlschema-to-MLForm series adaptation. -- `src/mlbundle/samples.py` — explicit feature-row selection and bounded cell conversion. -- `src/mlbundle/_sklearn/` — owned trust/skops/state/parameters/structure, transformer route and closed fitted-width/name proofs. Composite commitments/native structure bind the rebuilt Pipeline; all-sklearn structure stays null. -- `src/mlbundle/_xgboost/` — closed wrappers/objectives, native UBJSON preflight/loading, label binding, recoverable config, complete tree structure and validated prediction. -- `src/mlbundle/_catboost/` — closed numeric symmetric wrappers/losses, CBM preflight/in-memory loading, complete native state/structure and validated prediction. -- `src/mlbundle/_onnx/` — bounded protobuf/graph validation, unchanged bytes, native signatures/labels, CPU sessions and tabular prediction. -- `src/mlbundle/declarations.py` — class-label resolution and shared output-major target-name validation. -- `src/mlbundle/claims.py` — shared extracted claims and exact claim/structure comparison. -- `src/mlbundle/manifest.py` — manifest sections, cross-field validation and JSON boundaries. -- `src/mlbundle/integrity.py` — member SHA-256/size records and verification. -- `src/mlbundle/environment.py` — training declarations and distribution metadata capture. -- `src/mlbundle/adapters.py`, `_prediction_input.py`, `_prediction_decisions.py` — adapter/predictor protocols, input checks and bounded native class mapping. -- `src/mlbundle/registry.py` — closed lazy resolution of `_sklearn`, `_xgboost`, `_catboost` and `_onnx` adapters. -- `src/mlbundle/_container_rules.py` — shared source type, closed member sets/order and ceilings. -- `src/mlbundle/_zip_directory.py` — bounded ZIP/ZIP64 directory and extra-field validation. -- `src/mlbundle/_zip_members.py` — local headers/descriptors and bounded decompression with CRC. -- `src/mlbundle/container.py` — source bounds, validated in-memory reading and integrity checks. -- `src/mlbundle/writing.py` — integrity ownership, deterministic ZIP and atomic publication. -- `src/mlbundle/inspection.py`, `results.py` — metadata-only inspection and result records without adapter resolution. -- `src/mlbundle/records.py`, `_display.py` — record input/output and shared safe display. -- `src/mlbundle/_versions.py`, `_load_diagnostics.py` — bounded post-failure version hints. -- `docs/format.md` — version 1 wire specification; linked container, adapter/structure, ONNX and schema/sample sections are normative. -- `tests/` — one file per feature, with success and error cases together. -- `scripts/package_smoke.py`, `publish_check.py` — artifact checks and publish preflight. -- `pyproject.toml` and `uv.lock` — package metadata, tool configuration and lock. -- `.github/workflows/ci.yml`, `publish.yml` — shared CI and owner-approved publishing. -- `.pre-commit-config.yaml` — Ruff, Pyright and basic file hygiene. -- `README.md`, `SECURITY.md` and this file — project and contributor context. - -## Taste - -- One format definition beats synchronized copies. -- A closed adapter boundary beats dynamic loading. -- Early export errors beat surprises after upload. -- Honest metadata beats impressive but unverifiable visualisations. -- Simplicity never excuses weak validation or data safety. +Keep generated coverage and browser evidence out of changes. The conversation, +issues, source, tests and final diff are the work record. + +## Done + +Work is complete when every applicable path works, the owning layer contains +the rule, contracts remain intentional, obsolete behavior is gone, useful +regression coverage exists, relevant checks have run and documentation agrees. +Simplicity never excuses weaker validation or data safety. diff --git a/README.md b/README.md index cca674a..05065a7 100644 --- a/README.md +++ b/README.md @@ -1,297 +1,226 @@ -# mlbundle - -Package a trained model, manifest, locally inferred schema and structure in one `.mlbundle` file. -Export on the training machine and validate/load in the consuming application with the same Python library. - -mlbundle replaces three gaps in separate model/DataFrame uploads: +<p align="center"> + <img src="https://raw.githubusercontent.com/UlloaSP/mlbundle/main/brand/mlbundle.png" alt="" height="112" /> +</p> + +<h1 align="center"> + <picture> + <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/UlloaSP/mlbundle/main/brand/wordmark-dark.svg" /> + <img src="https://raw.githubusercontent.com/UlloaSP/mlbundle/main/brand/wordmark-light.svg" alt="MLbundle" height="52" /> + </picture> +</h1> + +<p align="center">Your trained model, schema and verified structure. One file.</p> + +<p align="center"> + <a href="./landing/"><img src="https://img.shields.io/badge/website-303039?style=flat&logo=astro&logoColor=ff6a1a" alt="Website source" /></a> + <a href="./docs/"><img src="https://img.shields.io/badge/docs-303039?style=flat&logo=readthedocs&logoColor=ff6a1a" alt="Documentation" /></a> + <a href="./playground/"><img src="https://img.shields.io/badge/playground-303039?style=flat&logo=stackblitz&logoColor=ff6a1a" alt="Playground" /></a> + <a href="https://pypi.org/project/mlbundle/"><img src="https://img.shields.io/badge/version-0.2.0-b73d00?style=flat&logo=pypi&logoColor=white&labelColor=303039" alt="Version 0.2.0" /></a> + <a href="https://github.com/UlloaSP/mlbundle"><img src="https://img.shields.io/badge/source-303039?style=flat&logo=github&logoColor=white" alt="Source on GitHub" /></a> + <a href="https://github.com/UlloaSP/mlbundle/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/UlloaSP/mlbundle/ci.yml?branch=main&style=flat&label=CI&labelColor=303039" alt="CI status" /></a> + <a href="./LICENSE"><img src="https://img.shields.io/badge/license-MIT-b73d00?style=flat&labelColor=303039" alt="MIT license" /></a> + <a href="https://eshkra.dev"><img src="https://img.shields.io/badge/part_of-eshkra-b73d00?style=flat&labelColor=303039" alt="Part of eshkra" /></a> +</p> + +MLbundle packages a trained model with its manifest, locally inferred input schema +and extractable structure in one compressed `.mlbundle` file. The same library +writes the bundle and validates it when loaded, preserving feature order, class +labels and output meaning across the training/runtime boundary. + +Use it to hand a model to [MLSuite](https://github.com/UlloaSP/mlsuite), inspect +its metadata or build your own consumer around the validated native model. + +## Why bundle a model? + +A model file alone leaves the next application to recover its inputs, labels, +environment and supporting metadata. Separate descriptions can drift from the +model they are supposed to describe. + +MLbundle keeps them together and checks model-derived claims against the loaded +model. Schema inference runs locally through [MLschema](https://github.com/UlloaSP/mlschema); +real training rows are included only when explicitly requested. Native formats +preserve the supported model without conversion. + +## Installation + +MLbundle **0.2.0** requires Python **3.14** for export with the schema extra. +The installation command for this release is: + +```bash +python -m pip install "mlbundle[sklearn,schema]==0.2.0" +``` -- **Pickle code execution:** closed adapters use skops, native XGBoost UBJSON, - CatBoost CBM or an existing ONNX file, without pickle/joblib/cloudpickle. -- **Missing training versions:** the manifest records Python, OS, architecture - and installed package versions alongside model and feature metadata. -- **Training data leaving the machine:** schema inference runs locally; - training rows are included only when explicitly requested as samples. +To use a local checkout, run `python -m pip install -e ".[sklearn,schema]"` +from its root. The base library requires Python >=3.14; schema inference uses +mlschema `>=0.3.0,<0.4` on Python 3.14 only. -**Pre-alpha v0.1.0a3, not yet published.** Version 0.1.0a2 is on PyPI. Format version 1 is -not frozen; bundles may need re-export. The security design has not had an -independent review: do not accept bundles from untrusted parties. -Requires **Python >=3.14**; tested on Windows/Linux. Schema requires Python 3.14. +Choose `xgboost`, `catboost` or `onnx` instead of `sklearn`, or install `[all]`. +Export always needs `schema`. Metadata-only inspection needs only the base +package; load and CLI verification need the matching model extra. -## Install and try scikit-learn +## Quick start -Install the pre-release with -`python -m pip install "mlbundle[sklearn,schema]==0.1.0a3"` once published. -Replace `sklearn` with `xgboost`, `catboost` or `onnx`, or use `[all]` for all dependencies. -Export needs the `schema` extra; reading does not. Omit the brackets for metadata-only inspection; load/verify needs the matching model extra. +Train a model, export its schema without including training rows, then inspect +and load the bundle: ```python +import pandas as pd from sklearn.tree import DecisionTreeClassifier -from mlbundle import export, inspect, load - -rows = [[0.0, 1.0], [1.0, 0.0], [0.1, 0.9], [0.9, 0.1]] -model = DecisionTreeClassifier(random_state=7).fit(rows, ["cold", "warm"] * 2) -export(model, "trained.mlbundle", feature_names=["temperature", "humidity"]) -bundle = load("trained.mlbundle") -assert isinstance(bundle.native_model, DecisionTreeClassifier) -assert bundle.native_model.predict(rows).tolist() == model.predict(rows).tolist() -assert bundle.class_labels == [["cold", "warm"]] -print(bundle.predict_proba([[0.0, 1.0]])) # [[1.0, 0.0]] -assert inspect("trained.mlbundle").manifest.model.library == "sklearn" -``` - -`export` returns metadata and requires `overwrite=True` for existing destinations. -`load` accepts paths, bytes or streams. Classifiers use `bundle.predict_proba`; -regressors use `bundle.predict`. Results are plain lists: output-major classifier -labels/probabilities, row-major regression values. `native_model` supports native -inference/explanations; mutating it invalidates the metadata verification. - -Native names win; otherwise origin is `declared`, `dataframe`, or `generated` (`feature_0` through `feature_{N-1}`). -For models without native names, pass a 2-D NumPy array or nested numeric rows in manifest feature order; declared names do not add name-based prediction. -`target_names=` and `metadata=` remain author declarations. -## Runtime records - -Load with the matching model extra, then use the bundle's own record mapping: +from mlbundle import export, inspect, load -```python -from mlbundle import load +training = pd.DataFrame( + { + "temperature": [0.0, 1.0, 0.1, 0.9], + "humidity": [1.0, 0.0, 0.9, 0.1], + "target": ["cold", "warm", "cold", "warm"], + } +) +model = DecisionTreeClassifier(random_state=7).fit( + training.drop(columns="target"), training["target"] +) -bundle = load("trained.mlbundle") -record = {"0": "0.0", "1": "true"} -print(bundle.predict_record(record)[0].probabilities) # (1.0, 0.0) -assert bundle.row_from_record(record) == [0.0, 1.0] # Also usable for explanations. -``` +export( + model, + "trained.mlbundle", + training_data=training, + target_names=["target"], +) -Model-origin keys are native feature names. Other origins use zero-based positions -as integers or canonical decimal strings; supplying both forms is an error. -Every feature is required as a key. Extra keys are ignored without iterating the -record, so work depends on feature count rather than record size. -Values accept finite Python numbers, booleans and decimal or true/false strings. -Strings strip ASCII whitespace and are limited to 64 characters; decimal forms -include `.5`, `1.` and exponents. `None` becomes NaN only when the schema field -permits missing values, using the group's requirement for one-hot options. -`PredictionInputError` names invalid keys without echoing values. Array/DataFrame -prediction retains its existing rules. Results are frozen `OutputPrediction` -records in output order, with classifier labels/probabilities or a regression value. -`predicted_label` is the report string for the native model's own class decision, -which may differ from the largest probability. It is `None` for regressors and -ONNX classifiers without a graph label output; mlbundle never substitutes argmax. -Name-selecting sklearn pipelines use a DataFrame and require pandas (included in -`schema`); other models use a numeric row, preserving native categorical handling. -`EnvironmentMismatchError` diagnoses a failed skops load with differing sklearn -versions. `InvalidManifestError` explains safe, different writer versions on a -failed manifest check. Neither diagnostic adds a compatibility gate. - -## XGBoost - -Install the `xgboost` extra. Train on encoded indices and supply the original -labels in probability-column order, nested even for one output: +metadata = inspect("trained.mlbundle") # No model deserialization. +print(metadata.manifest.features.names) # ["temperature", "humidity"] -```python -from xgboost import XGBClassifier -from mlbundle import export, load - -rows = [[0.0, 1.0], [1.0, 0.0], [0.1, 0.9], [0.9, 0.1]] -model = XGBClassifier(n_estimators=3, max_depth=2, n_jobs=1).fit(rows, [0, 1, 0, 1]) -export(model, "xgboost.mlbundle", class_labels=[["cold", "warm"]]) -bundle = load("xgboost.mlbundle") -assert isinstance(bundle.native_model, XGBClassifier) -assert bundle.native_model.predict(rows).tolist() == model.predict(rows).tolist() -assert bundle.predict_proba(rows) == model.predict_proba(rows).tolist() -assert bundle.class_labels == [["cold", "warm"]] +bundle = load("trained.mlbundle") # Recomputes native claims and structure. +result = bundle.predict_record({"temperature": 0.0, "humidity": 1.0})[0] +print(result.predicted_label) # cold +print(result.probabilities) # (1.0, 0.0) ``` -Labels/settings bind to a native **copy**. Load verifies consistency, not history. -Omitted labels use encoded indices/an existing binding; native predict stays encoded. -Standalone sklearn, CatBoost and regressors reject `class_labels=`; composites -delegate labels to the final native adapter. +The target column is excluded from the input schema. Add `sample_rows=N` only +when you intend to include the first N real feature rows. Without +`training_data=`, export infers a default schema from synthetic values. -Only recoverable native configuration is recorded; learning rate, max depth, -seed and thread count do not survive native XGBoost I/O and are not reconstructed. +Existing destinations require `overwrite=True`. Native feature names take +precedence; otherwise mappings use zero-based positions. `load` and `inspect` +accept paths, bytes or streams. The native model remains available as +`bundle.native_model`; mutating it invalidates the verified metadata. -## CatBoost +## Choose your operation -Install the `catboost` extra (>=1.2.10; includes matplotlib/plotly). Numeric, -single-target symmetric classifiers/regressors use native CBM and native labels: +| Operation | Purpose | +| --- | --- | +| `export(model, destination, ...)` | Validate a native round trip, infer the schema locally and write a bundle. | +| `inspect(source)` | Validate the container, manifest, hashes and declarations without loading a model. | +| `load(source)` | Load through a closed adapter and verify model-derived claims and structure. | +| `bundle.predict_proba(data)` | Classifier probabilities as plain lists. | +| `bundle.predict(data)` | Regressor predictions as plain lists. | +| `bundle.predict_record(record)` | Map one record through the schema and return frozen output-major results. | +| `bundle.row_from_record(record)` | Validate and order record values for native inference or explanations. | -```python -from catboost import CatBoostClassifier -from mlbundle import export, load - -rows = [[0.0, 1.0], [1.0, 0.0], [0.1, 0.9], [0.9, 0.1]] -model = CatBoostClassifier( - iterations=3, depth=2, verbose=False, allow_writing_files=False -).fit(rows, ["cold", "warm"] * 2) -export(model, "catboost.mlbundle") -bundle = load("catboost.mlbundle") -assert bundle.class_labels == [["cold", "warm"]] -assert bundle.predict_proba(rows) == model.predict_proba(rows).tolist() -``` - -`class_labels=`, categorical/text/embedding features, -Depthwise/Lossguide, custom Python objectives/metrics and other losses are rejected. -Classifier losses: Logloss, CrossEntropy, MultiClass. Regressors: RMSE, MAE, -Quantile, Huber, Poisson, MAPE. Complete trees/borders/NaN handling/importances -are verified on load. [The CatBoost contract](https://github.com/UlloaSP/mlbundle/blob/main/docs/format-catboost.md) details -wrapper recovery, native-parser limits and the private in-memory serializer. +The CLI exposes metadata inspection and full verification: -## Composite pipelines - -Install `[sklearn,xgboost]` or `[sklearn,catboost]`. Export the fitted Pipeline -as it is; transformers use skops and the final estimator uses its native adapter: - -```python -from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import StandardScaler -from xgboost import XGBClassifier -from mlbundle import export, load - -rows = [[0.0, 1.0], [1.0, 0.0], [0.1, 0.9], [0.9, 0.1]] -model = make_pipeline(StandardScaler(), XGBClassifier(n_estimators=3, n_jobs=1)) -model.fit(rows, [0, 1, 0, 1]) -export(model, "pipeline.mlbundle", class_labels=[["cold", "warm"]]) -bundle = load("pipeline.mlbundle") -assert bundle.predict_proba(rows) == model.predict_proba(rows).tolist() -assert bundle.native_model.predict(rows).tolist() == model.predict(rows).tolist() -assert bundle.class_labels == [["cold", "warm"]] +```shell +mlbundle inspect trained.mlbundle +mlbundle inspect trained.mlbundle --json +mlbundle verify trained.mlbundle ``` -Load rebuilds a real Pipeline after verifying both parts. Bundle probability labels -use XGBoost's bound originals; native classes/predict stay encoded. CatBoost keeps -native labels. Prefixes include scalers, PCA, SimpleImputer (indicators/empty columns), -KBinsDiscretizer, PolynomialFeatures, univariate selectors, OneHotEncoder/OrdinalEncoder, -identity/closed NumPy FunctionTransformer, nested native-free Pipeline and ColumnTransformer. -Each needs a fitted, data-free width proof. Unknown widths, FeatureUnion/KNNImputer, -slice/callable/scalar selectors, caching, metadata routing and other native placements -remain rejected. Step names/outer options are declarations; trees address transformed -features, without original-feature attribution. Schema/dtypes/samples describe original -inputs; six members permit schema plus explicit samples. See [the pipeline contract](https://github.com/UlloaSP/mlbundle/blob/main/docs/format-pipeline.md). - -## Existing ONNX files - -Install the `onnx` extra. This example assumes your existing `trained.onnx` is -a self-contained tabular classifier with two numeric features and two probability -columns. Labels on a label-free graph are count-checked declarations: - -```python -from pathlib import Path -from mlbundle import export, load - -original = Path("trained.onnx").read_bytes() -export("trained.onnx", "onnx.mlbundle", class_labels=[["cold", "warm"]]) -bundle = load("onnx.mlbundle") -assert bundle.native_model.data == original -assert bundle.native_model.session.get_providers() == ["CPUExecutionProvider"] -assert bundle.class_labels == [["cold", "warm"]] -print(bundle.predict_proba([[0.0, 0.0]])) +Inspection does not prove that metadata describes the model. Full verification +loads the model and compares its claims. Neither operation establishes authorship +or training history. + +## Supported models + +| Library | Storage | Support boundary | +| --- | --- | --- | +| scikit-learn | skops | Closed fitted classifier/regressor families and supported transformers. | +| XGBoost | Native UBJSON | Exact single-target tree wrappers and closed objectives. | +| CatBoost | Native CBM | Exact numeric, single-target symmetric classifiers/regressors. | +| ONNX | Original ONNX bytes | Self-contained tabular models with supported signatures; no conversion. | +| Composite pipelines | skops + UBJSON or CBM | Supported sklearn preprocessing with an XGBoost/CatBoost final estimator. | + +Custom types fail at export. Sklearn and CatBoost own their class labels; +XGBoost can bind declared labels to a native copy. ONNX has no visualization +structure, and all-sklearn Pipeline structure is null. The +[adapter contracts](./docs/format-adapters.md) define the exact support limits. + +## Some notes + +Package versions and wire-format versions are independent. Format 1 is not frozen; +incompatible schema shapes require re-export. The security design has not had an +independent review; do not accept bundles from untrusted parties. + +The reader bounds ZIP/JSON data, verifies integrity and uses closed native loaders. +Hashes detect corruption, not deliberate tampering. Native parsers and inference +still need process isolation and application-owned resource limits. +Read [SECURITY.md](./SECURITY.md) for guarantees, limits and private reporting. + +Training rows stay local by default, but feature names, categories, labels and +learned structure can reveal information. Schemas and environment records remain +author declarations. Model training, hosting, signing and conversion are outside +the library's scope. + +## Documentation + +The [documentation site](./docs/) includes installation, adapter guides, record +prediction and API reference. The normative contracts remain in this repository: + +- [Bundle format and verification](./docs/format.md) +- [Schema, mappings and explicit samples](./docs/format-schema.md) +- [Supported adapters and structure](./docs/format-adapters.md) +- [Sklearn state and trusted types](./docs/format-state.md) +- [Composite pipelines](./docs/format-pipeline.md) +- [ONNX layouts and bounds](./docs/format-onnx.md) + +The [playground](./playground/) provides editable examples and inspection of the +generated `.mlbundle` file. It runs scikit-learn examples in the browser and +inspects uploaded files without loading their models. + +## Contributing + +The Python library uses [uv](https://docs.astral.sh/uv/) and Python 3.14: + +```bash +uv python install 3.14 +uv sync --locked --all-extras +uv run pre-commit install +uv run ruff check . +uv run ruff format --check . +uv run pyright +uv run pytest +uv build ``` -Paths and bytes are preserved unchanged; a ModelProto is serialized once. -There is no conversion. `native_model` contains the original `data` and CPU -`session`. Native graph labels are verified; supplied labels must agree with -them. Without native/supplied labels, encoded indices are generated. ONNX has -no visualization structure. See [the exact ONNX contract](https://github.com/UlloaSP/mlbundle/blob/main/docs/format-onnx.md). - -## Local schema and explicit samples +Tests use the installed package and enforce 80% branch-inclusive coverage. +CI validates the built wheel on Windows and Linux. Read [AGENTS.md](./AGENTS.md) +for contribution rules and [publishing](./docs/publishing.md) for the owner-approved +release workflow. -Install a model extra plus `schema` on Python 3.14. Supply a nonempty pandas -DataFrame with string feature names in model input order. `target_names` has one -unique name per output; every target column must exist and is excluded. Without it the frame contains features only. +The landing, docs and playground use [Vite+](https://viteplus.dev/guide/) and a +pnpm workspace. With `vp` installed, run from the repository root: -```python -import pandas as pd -from sklearn.tree import DecisionTreeClassifier -from mlbundle import export, inspect, load - -training = pd.DataFrame( - [[0.0, 1.0, "cold"], [1.0, 0.0, "warm"]] * 2, - columns=["temperature", "humidity", "target"], -) -features = training.drop(columns="target") -model = DecisionTreeClassifier(random_state=7).fit(features, training["target"]) -export(model, "schema.mlbundle", training_data=training, target_names=["target"]) -view = inspect("schema.mlbundle") -assert view.manifest.features.dtypes == ["float64", "float64"] -assert view.schema["reports"][0]["labels"] == ["cold", "warm"] -assert view.samples is None -assert load("schema.mlbundle").schema == view.schema -# Samples are real data: explicitly include the first two feature rows. -export(model, "sampled.mlbundle", training_data=features, sample_rows=2) -assert inspect("sampled.mlbundle").samples == features.head(2).to_dict("records") -``` - -`schema.json` is required and uses schema version `2`, with `source` equal to -`training_data` or `default`. Without a frame, mlschema infers fields from one -synthetic row of float `1.0` values; training dtypes remain null. `onehot_separator=` -defaults to `__` and is stored in the schema (1–16 characters). `color__red`/`color__blue` -binary columns can become one category field. Native-name mappings are strings; all other origins map by zero-based feature position. Reports map by output position, -including `0` for one output, with string display labels; manifest class labels retain -their original types. Frame names must match native names in order; `feature_names=` cannot rename/reorder a supplied frame. -Training dtypes are declarations, separate from verified model dtypes. Inspect/load -validate schema mappings, reports and sample keys without pandas or mlschema. -Bundles written by 0.1.0a1 without schema, or with schema version `1`, must be re-exported. - -**Data leaving the machine:** names/dtypes, field kinds/nullability, dtype-based -steps, categories (including unused), one-hot suffixes, pair-axis metadata and -class labels. No inferred ranges, means, counts or arbitrary text/example rows. -Names/categories and learned models/structure can reveal training information. -Explicit `sample_rows=N` includes the first N feature rows, without target/index; -N must be positive and within the frame. See [schema and samples](https://github.com/UlloaSP/mlbundle/blob/main/docs/format-schema.md). - -## Inspect, load and the CLI - -`inspect` validates archive layout, limits, manifest, hashes and schema/sample -declarations **without importing any model library**. It cannot attest that -metadata or structure matches the model. `load` additionally deserializes through -the closed adapter and recomputes native claims and structure before returning -the model and predictor. Export checks the same native round trip before writing. - -```shell -mlbundle inspect trained.mlbundle -mlbundle inspect trained.mlbundle --json -mlbundle verify trained.mlbundle +```bash +vp install +vp run check +vp run build ``` -Inspection prints metadata, members/hashes and declarations. Human values escape -terminal controls and truncate at 512 characters; `--json` is lossless ASCII-escaped -manifest JSON. `verify` requires the model extra. Errors are one bounded stderr -line without bundle content/traceback. Exit codes: **0** success, **1** bundle or -dependency rejection (`MLBundleError`), **2** usage/I/O failure. - -## Supported models and limits - -- **Sklearn:** fitted classifiers exposing `predict_proba` and regressors with recoverable feature/output counts. Tested families include trees/ensembles, linear/regularized/robust/GLM models, SVMs, PLSRegression, naive Bayes, LDA/QDA, dense brute-force neighbors, NearestCentroid, MLP and MultiOutput/RegressorChain. SimpleImputer/KNNImputer, scalers, KBinsDiscretizer, categorical encoders, univariate selectors and closed NumPy FunctionTransformer functions round-trip, including nested ColumnTransformer pipelines; see [the exact lists](https://github.com/UlloaSP/mlbundle/blob/main/docs/format-state.md). All-sklearn Pipeline structure stays null; composite trees expose the inner boundary. See - [tested configurations](https://github.com/UlloaSP/mlbundle/blob/main/tests/test_sklearn_families.py) and [core round trips/rejections](https://github.com/UlloaSP/mlbundle/blob/main/tests/test_sklearn.py). -- **Sklearn rejections:** custom estimators/subclasses, functions/types outside the closed lists (including lambdas and user functions), compound fitted dtypes, classifiers without probabilities, multilabel/ClassifierChain layouts, unsupported native placements, and untrusted nested storage such as fitted sparse neighbor arrays. HistGradientBoosting uses untrusted `TreePredictor` storage and is rejected. -- **Sklearn versions:** the closed class/attribute catalog is audited/tested against **1.9.1 with skops 0.16.0**, not every version allowed by `>=1.6`. Unknown state fails; matching export/load sklearn versions are recommended. Configurations must satisfy the catalog and round trip; see [state ownership](https://github.com/UlloaSP/mlbundle/blob/main/docs/format-state.md). -- **XGBoost:** exact XGBClassifier/XGBRegressor, one target, native gbtree - (including XGBoost 3.4.1 dart normalized to gbtree). Closed probability/regression objectives; raw boosters, RF wrappers, subclasses, ranking/survival/custom objectives and unsupported storage are rejected. At most 100,000 trees/depth 14, with complete state/dumps; see [the mapping](https://github.com/UlloaSP/mlbundle/blob/main/docs/format-adapters.md#implemented-xgboost-mapping). -- **ONNX:** fixed-width rank-two numeric inputs; one regression/probability target, optional native labels and dynamic/one-row batches. Unsupported layouts, external data/custom domains/functions and runtime/training configuration fail before CPU sessions. Tested with ONNX 1.23.0/Runtime 1.30.0; see [ONNX bounds](https://github.com/UlloaSP/mlbundle/blob/main/docs/format-onnx.md). - -Archive ceilings: **6 members**, **512 MiB** archive/compressed/member bytes, -**1 GiB** aggregate uncompressed bytes, ratio **200**, JSON depth **32**. Manifest: **8 MiB**; structure: **256 MiB**; schema/samples: **16 MiB each**. Features/classes: at most **100,000**; outputs: **1,024** (XGBoost/CatBoost/ONNX: one). Callers may only lower `ArchiveLimits`. Native [state](https://github.com/UlloaSP/mlbundle/blob/main/docs/format-state.md)/[ONNX](https://github.com/UlloaSP/mlbundle/blob/main/docs/format-onnx.md) bounds do not cap process memory, prediction rows or CPU. - -## Bundle contents and security - -Every bundle contains `manifest.json`, `schema.json` and one native member, or the declared -`model.skops` + `model.ubj`/`model.cbm` composite pair. Single-step composites use only the leaf. Optional members are `structure.json` -(required when the adapter extracts structure) and explicitly requested -`samples.json`. No training DataFrame or arbitrary files are included. -The manifest records identity/time/writer, model parameters/counts/state, -ordered features/dtypes/origin, target labels/names, environment, member -SHA-256/sizes, references and free-form user metadata. The normative -[format specification](https://github.com/UlloaSP/mlbundle/blob/main/docs/format.md) owns these records and verification rules. - -Treat bundles as untrusted. The reader validates ZIP layout, allowed members, -limits, strict JSON/manifest and hashes; adapters use closed trust/loading paths -and recompute claims. **Hashes detect corruption, not deliberate tampering**; -there is no signing or proof of authorship, training history, quality or fairness. -Schema, samples and training-environment declarations are not authenticated. -Native parsers/inference require process isolation, memory/CPU/row limits and -maintained dependencies. See [SECURITY.md](https://github.com/UlloaSP/mlbundle/blob/main/SECURITY.md) for guarantees and reporting. - -## Development - -From the root: `uv python install 3.14`, `uv sync --locked --all-extras`, `uv run pre-commit install`. Default dev installs every extra; base: `uv sync --no-dev`. Pydantic v2 is the sole core dependency; model/schema imports are lazy. Schema uses mlschema `>=0.2.1,<0.3` on 3.14 only (0.2.1 excludes 3.15). `uv.lock` fixes tested versions; dependency edits require `uv sync` and committing the updated lockfile. - -Run `uv run ruff check .`, `uv run ruff format --check .`, `uv run pyright`, `uv run pytest` and `uv build`. Tests execute every fenced README example and write terminal/XML coverage with an 80% branch-inclusive minimum. CI checks lint/types and tests the same built wheel on Ubuntu/Windows, including clean base installs. The owner, or an agent on the owner's explicit request for that version, runs manual [publishing](https://github.com/UlloaSP/mlbundle/blob/main/docs/publishing.md) from `main` with `pypi_env` approval. Read [AGENTS.md](https://github.com/UlloaSP/mlbundle/blob/main/AGENTS.md). Agents never run or approve it on their own initiative or add publish paths, tokens or secrets. Releases, version tags, conversions, per-model environments, signing and additional model libraries remain outside scope. The project is MIT licensed. +Start a site with `vp run dev:landing`, `vp run dev:docs` or +`vp run dev:playground`. Their local ports are 4340, 4341 and 10021 respectively. +Each site also accepts `vp run dev`, `vp run build` and `vp run preview` from its +directory. Astro sites run their framework CLI through `vp exec`; the playground +uses Vite+ directly. `vp run check` runs frontend lint, landing formatting, all +three sites' type checks, and the docs and playground unit tests. Runtime and +browser tests live in `playground/`. + +Actions runs Python and frontend checks on pushes and PRs to `main` and `develop`. +After all checks pass on `main`, it deploys the three tested site builds to +Cloudflare. Manual **CI Pipeline** runs on `main` use the same gates and can +redeploy without publishing Python. A change to `project.version` on `main` +automatically starts the PyPI workflow after its version and branch preflight. +See [website deployment](./docs/deployment.md) and [publishing](./docs/publishing.md). + +## License + +MIT. The design takes ideas from MLflow's versioned manifest and native storage, +not its code. diff --git a/SECURITY.md b/SECURITY.md index f91dc70..5f1ed08 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -1,6 +1,6 @@ # Security policy -The v0.1.0a3 development pre-alpha has not had an independent security review; +MLbundle 0.2.0 has not had an independent security review; do not accept bundles from untrusted parties. Format version 1 is not frozen. It implements strict manifest validation, secure container reading/writing, metadata-only `inspect`, export/load, sklearn, XGBoost, CatBoost and ONNX adapters, local schema inference, @@ -291,5 +291,4 @@ behavior, and the impact. Share malicious bundles or sensitive details through that private report rather than a public issue or pull request. Remove personal or confidential training data from any reproducer you submit. -Version 0.1.0a2 is published; 0.1.0a3 is not yet published. Report the affected -version and commit you tested. +Report the affected version and commit you tested. diff --git a/brand/README.md b/brand/README.md new file mode 100644 index 0000000..4b83083 --- /dev/null +++ b/brand/README.md @@ -0,0 +1,14 @@ +# MLbundle identity + +Original mark copied unchanged from `eshkra/src/assets/marks/mlbundle.png` +(708 × 960). The PNG retains its original resolution and embedded provenance. + +Outlined wordmarks use Mona Sans from the family's self-hosted Latin subset: ML at +weight 800 and width 125%, bundle at weight 300 and width 100%, matching MLform and +MLschema glyph for glyph. 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z(>3;aA}T2j;bxh~fBXkO3pz}ZHd)Xvwt*AXJlTa%|6{*v_4~EDa<l&){(c<4*_`Ix z8?5(@4imlW2j4%l`#&H4e@%7ucRFBNoOmH#$ln0~fI5<y3ke&DM|*jBcm|*i@dR8T zo&-4HLI_@H?^CcK90`U&2cZcbB(yio7fnE)f!SQ}3<>fMK*It(2>viN48a5MizT69 z1gxhgE(qoc^B~~Z17IFOFdV@n2!m$dJWs-iA$WWMmMz6I0!H%vF&i61Vqa(iEDTHX zg^}1}iFgkJI>;*mrlzf>t#=x(bsDaJ@+|umHZYJq(}OLQfF@$eXzw#ZXFr`|e*j=* MW^4M`<Z8@+0IQ+<ZU6uP literal 0 HcmV?d00001 diff --git a/brand/mona-sans-LICENSE.txt b/brand/mona-sans-LICENSE.txt new file mode 100644 index 0000000..fd6728b --- /dev/null +++ b/brand/mona-sans-LICENSE.txt @@ -0,0 +1,93 @@ +Copyright 2022 The Mona Sans Project Authors (https://github.com/github/mona-sans), with Reserved Font Name "Mona" MonaSans-Italic[wdth,wght].ttf: Copyright 2022 The Mona Sans Project Authors (https://github.com/github/mona-sans), with Reserved Font Name "Mona" + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +http://scripts.sil.org/OFL + + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting -- in part or in whole -- any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE +COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL +DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM +OTHER DEALINGS IN THE FONT SOFTWARE. diff --git a/brand/wordmark-dark.svg b/brand/wordmark-dark.svg new file mode 100644 index 0000000..689867c --- /dev/null +++ b/brand/wordmark-dark.svg @@ -0,0 +1,14 @@ +<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 306.88 76" role="img" aria-label="MLbundle"> +<title>MLbundle +Mona Sans: ML weight 800 width 125%; bundle weight 300 width 100%. SIL OFL 1.1. + + + + + + + + + + + diff --git a/brand/wordmark-light.svg b/brand/wordmark-light.svg new file mode 100644 index 0000000..6e250e3 --- /dev/null +++ b/brand/wordmark-light.svg @@ -0,0 +1,14 @@ + +MLbundle +Mona Sans: ML weight 800 width 125%; bundle weight 300 width 100%. SIL OFL 1.1. + + + + + + + + + + + diff --git a/docs/.gitignore b/docs/.gitignore new file mode 100644 index 0000000..ddce69b --- /dev/null +++ b/docs/.gitignore @@ -0,0 +1,3 @@ +node_modules/ +dist/ +.astro/ diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..7265737 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,35 @@ +# mlbundle documentation site + +The Astro Starlight site for `docs.mlbundle.eshkra.dev`, package `mlbundle-docs` in the repository's +pnpm workspace. Run every command from the repository root with Vite+ (`vp`) and Node.js 22.18+, +24.11+ or 26+: + +```shell +vp install --frozen-lockfile +vp run mlbundle-docs#dev # http://127.0.0.1:4341/ +vp run mlbundle-docs#build # astro check, then a static build in docs/dist +``` + +In development the header links point to the local landing (`4340`) and playground (`10021`); +built output links to the production hosts. + +From `docs/`, use `vp run dev`, `vp run build` or `vp run preview`. These scripts +invoke Astro through `vp exec`; bare `vp dev` and `vp build` run Vite's own +commands and do not build an Astro site. + +## Single-sourced contract + +The Markdown files beside this README (`format*.md`) and the root `SECURITY.md` are the normative +contract. The site renders them in place through the `spec` collection in `src/content.config.ts`, +mapped to routes in `src/spec.ts`; relative links become site routes or GitHub links. Edit those +files, never a copy. `publishing.md` is maintainer guidance and is not rendered. + +Guides live in `src/content/docs/`. Their Python fences are checked by `uv run ruff format --check`. +The link resolver has a self-test: + +```shell +vp run mlbundle-docs#test +``` + +The brand mark is read from the repository's `brand/` directory. Actions builds, +checks and deploys this site with the other frontends; see [website deployment](deployment.md). diff --git a/docs/astro.config.ts b/docs/astro.config.ts new file mode 100644 index 0000000..6284329 --- /dev/null +++ b/docs/astro.config.ts @@ -0,0 +1,73 @@ +import { defineConfig } from "astro/config"; +import starlight from "@astrojs/starlight"; +import { fileURLToPath } from "node:url"; +import { sidebar } from "./src/navigation"; + +const repo = (path: string) => + fileURLToPath(new URL(`../${path}`, import.meta.url)).replaceAll("\\", "/"); + +export default defineConfig({ + site: "https://docs.mlbundle.eshkra.dev", + trailingSlash: "always", + devToolbar: { enabled: false }, + vite: { + // The docs read the shared brand assets at the repository root. + server: { + fs: { + allow: [fileURLToPath(new URL(".", import.meta.url)), repo("brand")], + }, + }, + }, + integrations: [ + starlight({ + title: "mlbundle", + description: + "Pickle-free, verifiable model bundles with a manifest, a local schema and model structure.", + components: { + Head: "./src/components/BrandHead.astro", + SiteTitle: "./src/components/BrandTitle.astro", + SocialIcons: "./src/components/SocialIcons.astro", + Footer: "./src/components/Footer.astro", + }, + social: [ + { + icon: "github", + label: "GitHub", + href: "https://github.com/UlloaSP/mlbundle", + }, + ], + editLink: { + baseUrl: "https://github.com/UlloaSP/mlbundle/edit/main/docs/", + }, + expressiveCode: { + // The landing's code themes, switched with the documentation theme. + themes: ["github-dark-default", "github-light-default"], + styleOverrides: { + borderRadius: "12px", + borderColor: "var(--mb-border)", + codeFontFamily: "var(--brand-font-mono)", + uiFontFamily: "var(--brand-font-sans)", + codeFontSize: "0.8125rem", + codeLineHeight: "1.7", + frames: { + shadowColor: "transparent", + editorTabBarBackground: "var(--mb-subtle)", + editorActiveTabBackground: "var(--mb-code-bg)", + editorActiveTabIndicatorTopColor: "var(--mb-ember)", + editorActiveTabIndicatorBottomColor: "transparent", + editorBackground: "var(--mb-code-bg)", + terminalTitlebarBackground: "var(--mb-subtle)", + terminalBackground: "var(--mb-code-bg)", + }, + }, + }, + customCss: [ + "./src/styles/typography.css", + "./src/styles/theme.css", + "./src/styles/content.css", + "./src/styles/home.css", + ], + sidebar, + }), + ], +}); diff --git a/docs/deployment.md b/docs/deployment.md new file mode 100644 index 0000000..41487f1 --- /dev/null +++ b/docs/deployment.md @@ -0,0 +1,85 @@ +# Website deployment + +The three websites deploy as Cloudflare Workers Static Assets. They need no +server-side Python: the playground runs Python in the visitor's browser. + +| Site | Worker | Domain | +| --- | --- | --- | +| Landing | `mlbundle-landing` | `mlbundle.eshkra.dev` | +| Documentation | `mlbundle-docs` | `docs.mlbundle.eshkra.dev` | +| Playground | `mlbundle-playground` | `playground.mlbundle.eshkra.dev` | + +Each site's `wrangler.jsonc` owns its worker name, custom domain, asset directory +and routing. Astro sites use trailing slashes and their generated 404 pages. +Cloudflare manages the custom domains and certificates during deployment. + +## Triggers and checks + +`.github/workflows/ci.yml` runs on pushes and pull requests targeting `main` or +`develop`, and supports **Run workflow** in GitHub Actions. + +- Python gates build wheel/sdist, validate metadata, lint, check types and test + the built wheel and base install on Ubuntu and Windows. +- The reusable `sites.yml` runs frontend lint, landing formatting, Astro/TypeScript + checks, docs link tests, playground unit tests and real Pyodide example tests. + It builds all three sites, validates Wrangler configuration without uploading, + and runs Chromium tests on desktop and mobile against the built playground. +- Only successful frontend runs upload `landing-build`, `docs-build` and + `playground-build`. Browser failures retain their traces for seven days. +- After **all** Python and frontend jobs pass, pushes to `main` and manual CI + runs on `main` call `deploy-sites.yml`. PRs, forks and `develop` never deploy. + +Deployment downloads each tested artifact from the same run; it does not rebuild. +It uses the locked Wrangler dependency through `vp exec`, deploys each site to +its own GitHub environment, and checks the resulting homepage title over HTTPS. +Deployments for the same site are serialized and are not cancelled midway. + +`publish.yml` runs reuse these checks, but skip website deployment. PyPI +publication starts automatically when a push to `main` changes +`project.version`, runs only from `main` through `pypi_env`, and is independent +of website deployments. No tags or GitHub releases are created; see +[publishing](publishing.md). + +## GitHub and Cloudflare setup + +The repository's Actions secrets must contain `CLOUDFLARE_API_TOKEN` and +`CLOUDFLARE_ACCOUNT_ID`. The token must authorize Workers deployments and custom +domain management for `eshkra.dev`. The zone must belong to that account and be +active in Cloudflare. Keep credentials in Actions; +never put them in a Wrangler file or commit them. + +The `landing`, `docs` and `playground` GitHub environments allow deployment only +from the `main` branch. They have no manual review gate; passing CI permits the +automatic website deployment. `pypi_env` is also limited to `main` and has no +manual review gate. + +Secrets being present does not prove their permissions or domain ownership. +The first successful Actions deployment verifies that external configuration. + +## Local validation and redeployment + +From the repository root: + +```sh +vp install --frozen-lockfile +vp run check +vp run mlbundle-playground#test:runtime +vp run build +vp run check:deploy +vp run mlbundle-playground#test:e2e:built +``` + +Install Chromium first with `vp exec playwright install chromium` from +`playground/`. `check:deploy` uses `wrangler deploy --dry-run`; it never uploads. +Node 24.14.1 and the Vite+ version pinned in `package.json` are used in Actions. + +To redeploy current `main`, select **CI Pipeline → Run workflow → main**. This +reruns every gate and does not publish to PyPI. For a rollback, revert the website +change through the usual review process and merge to `main`; the new CI run +deploys the reverted build. The repository files must be committed and pushed +before GitHub can execute a changed workflow. + +The setup follows the official [Vite+ action](https://github.com/voidzero-dev/setup-vp), +[reusable workflow](https://docs.github.com/en/actions/how-tos/reuse-automations/reuse-workflows) +and [Wrangler configuration](https://developers.cloudflare.com/workers/wrangler/configuration/) +documentation. diff --git a/docs/format-pipeline.md b/docs/format-pipeline.md index 74e20b8..b2e3ac2 100644 --- a/docs/format-pipeline.md +++ b/docs/format-pipeline.md @@ -1,6 +1,6 @@ # Version 1 composite pipelines -Normative section of [the format contract](format.md). This pre-release format +Normative section of [the format contract](format.md). This format extension preserves reading of standalone bundles. Older readers reject composites. ## Representation and declarations diff --git a/docs/format-schema.md b/docs/format-schema.md index 0e319e5..dae7067 100644 --- a/docs/format-schema.md +++ b/docs/format-schema.md @@ -1,7 +1,7 @@ # Format 1 schema v2 and samples Normative section of [the format contract](format.md). Local inference uses -mlschema >=0.2.1,<0.3, tested with 0.2.1. Fields, source and separator are untrusted +mlschema >=0.3.0,<0.4, validated against local 0.3.0. Fields, source and separator are untrusted declarations; integrity cannot authenticate the training data or its history. ## Export inputs and defaults @@ -35,8 +35,8 @@ without builders, overrides or custom kinds. The separator is stored and read from the schema; consumers must not assume the default. Model-origin fields retain name mappings. Dataframe/declared inference preserves -named columns for grouping, then rewrites parent/one-hot `mappedTo` values -to their zero-based feature position. Series columns have no mappings. Generated +named columns for grouping, then calls `mlschema.with_positional_mappings(fields, names)` to convert +parent/one-hot targets to their zero-based feature position. Series columns have no mappings. Generated origin is inferred on positional columns directly. Labels remain feature names. For models without native names, prediction accepts 2-D NumPy arrays or nested numeric rows in manifest feature order. Adapter-supported sparse arrays and @@ -76,8 +76,8 @@ All five keys are required and unknown keys are forbidden. `source` is exactly and unknown versions raise bounded `InvalidSchemaError` requiring re-export. A missing schema member raises bounded `InvalidContainerError` saying the bundle predates the required schema and must be re-exported, through shared member rules. -Thus 0.1.0a1 bundles without schema or with schema v1 are incompatible with 0.1.0a2. -Format version remains `1`, whose pre-release wire contract is not frozen. +Thus 0.1.0a1 bundles without schema or with schema v1 require re-export for 0.2.0. +Format version remains `1`, whose wire contract is not frozen. Arrays preserve order. `fields` has 1–100,000 entries and `reports` 1–1,024. Typed records use strict types, finite numbers and no arbitrary extensibility. @@ -99,12 +99,12 @@ a top-level ordinary label must equal the feature name at that index. | `series` | `columns`: exactly two scalar subfields, with `id` equal to `field1` then `field2`, and no `mappedTo`; no nested series/one-hot groups. Labels describe pair axes. | | `onehot-category` | Special shape below. | -In 0.1.0a3 the `series` shape changed from `field1`/`field2` to MLForm's +In 0.2.0 the `series` shape changed from `field1`/`field2` to MLForm's `columns` while format 1 is not frozen. Schema version stays `"2"`: no bundle written before this change contains a series field, so a version bump would invalidate every 0.1.0a2 bundle for a shape none uses. Readers reject the old shape. -The exporter adapts mlschema's pairs in one small function until mlschema emits -this shape. Parent `label`, `required` and `mappedTo` remain unchanged: +MLschema emits this shape directly. Parent `label`, `required` and `mappedTo` +remain unchanged: ```json {"kind":"series","label":"pair","mappedTo":0,"required":true, diff --git a/docs/format-state.md b/docs/format-state.md index 977ec78..3675dca 100644 --- a/docs/format-state.md +++ b/docs/format-state.md @@ -1,7 +1,7 @@ # Version 1 prediction state and bounded native work -Normative section of [the format contract](format.md). Version 1 is pre-release -and not yet frozen. Bundles lacking the required prediction-state records +Normative section of [the format contract](format.md). Version 1 is not frozen. +Bundles lacking the required prediction-state records fail full claim comparison. ## Sklearn state ownership diff --git a/docs/format.md b/docs/format.md index 7a5d1ad..0bd99bc 100644 --- a/docs/format.md +++ b/docs/format.md @@ -2,10 +2,10 @@ This document defines the wire contract for `format_name = "mlbundle"` and `format_version = "1"`. Format versions are independent of package versions. -"Must" denotes a compatibility or security requirement. This pre-release +"Must" denotes a compatibility or security requirement. This contract is implemented by the manifest, secure container, inspect/export/load, sklearn/XGBoost/CatBoost/ONNX adapters, local schema, samples and prediction-state checks. -Version 1 is not frozen. Releases 0.1.0a2/a3 require schema v2; incompatible manifest/schema shapes require re-export. Valid a2 bundles without series remain compatible. The inspect/verify CLI uses these public APIs. +Version 1 is not frozen. MLbundle 0.2.0 requires schema v2; incompatible manifest/schema shapes require re-export. Valid 0.1.0a2 bundles without series remain compatible. The inspect/verify CLI uses these public APIs. The normative container rules are in [Container and resource limits](format-container.md). The normative structure, adapter and result rules are in [Adapters and structure](format-adapters.md). diff --git a/docs/package.json b/docs/package.json new file mode 100644 index 0000000..bfa00ff --- /dev/null +++ b/docs/package.json @@ -0,0 +1,27 @@ +{ + "name": "mlbundle-docs", + "version": "0.0.0", + "private": true, + "type": "module", + "scripts": { + "dev": "vp exec astro dev --host 127.0.0.1 --port 4341", + "start": "vp run dev", + "build": "vp run typecheck && vp exec astro build", + "check": "vp run typecheck", + "preview": "vp exec astro preview --host 127.0.0.1 --port 4341", + "typecheck": "vp exec astro check", + "test": "vp exec node --test src/spec.test.ts" + }, + "dependencies": { + "@astrojs/starlight": "0.42.1", + "astro": "7.3.2", + "sharp": "0.35.5" + }, + "devDependencies": { + "@astrojs/check": "0.9.10", + "typescript": "6.0.3" + }, + "engines": { + "node": "^22.18.0 || ^24.11.0 || >=26.0.0" + } +} diff --git a/docs/publishing.md b/docs/publishing.md index 6a8071a..ef847aa 100644 --- a/docs/publishing.md +++ b/docs/publishing.md @@ -1,61 +1,48 @@ -# Manual pre-alpha publishing +# Publishing to PyPI -Version `0.1.0a3` is the next unpublished pre-alpha; `0.1.0a2` is on PyPI. -Format version 1 is not frozen; incompatible manifest/schema shapes require -re-export. Valid a2 bundles remain compatible, with the series exception documented -in [the schema contract](format-schema.md). The security design has not had an +Version `0.2.0` is prepared by this checkout. Merging a version change into +`main` automatically starts the PyPI workflow; no one needs to start a release +run manually. Format version 1 is not frozen, so incompatible manifest/schema +shapes require re-export. Valid a2 bundles remain compatible, as documented in +[the schema contract](format-schema.md). The security design has not had an independent review; do not accept bundles from untrusted parties. -The owner, or an agent on the owner's explicit request for that specific version, -runs this workflow. Agents never run or approve it on their own initiative. -There is no API token, repository secret, tag, GitHub release or other publish path. - -## Publishing steps - -The owner, or an agent on the owner's explicit request, follows these steps: - -1. Review and merge the change into `develop`, then bring it into `main` through - the normal reviewed integration process. The workflow refuses any other ref. -2. Confirm the registered PyPI pending publisher at - [account publishing](https://pypi.org/manage/account/publishing/) has these - exact values: project `mlbundle`, owner `UlloaSP`, repository `mlbundle`, - workflow `publish.yml`, environment `pypi_env`. Confirm the GitHub `pypi_env` - environment still requires `UlloaSP` approval and permits only `main`. -3. Open [Publish to PyPI](https://github.com/UlloaSP/mlbundle/actions/workflows/publish.yml), - choose **Run workflow**, select **main**, and run it once. -4. Wait for the build/CI jobs to succeed. Review the run's commit and downloaded - `distributions` artifact, containing the `0.1.0a3` wheel and source archive. - Approve the `pypi_env` deployment when satisfied. This approval permits upload. -5. Check [mlbundle on PyPI](https://pypi.org/project/mlbundle/0.1.0a3/) and install - with `python -m pip install "mlbundle[sklearn,schema]==0.1.0a3"` on Python 3.14. - -A pending publisher does not reserve a name until a successful upload. The -[PyPI pending-publisher documentation](https://docs.pypi.org/trusted-publishers/creating-a-project-through-oidc/) -explains this first-upload step. If publishing fails or only one artifact uploads, -inspect the PyPI state before taking further action; the preflight rejects an -existing version and the workflow does not skip existing files. - -## What the workflow checks - -`publish.yml` has two jobs. Its `build` job calls `ci.yml` from the same commit, -with publishing preflight enabled. That preflight checks `refs/heads/main`, reads -the version from `pyproject.toml`, and requires HTTP 404 from the exact PyPI -version endpoint. An existing version, other HTTP errors or a network failure -stop the run. One concurrency group prevents overlapping publish runs. - -Reusable CI builds one wheel and source archive with Python 3.14, uv 0.12.19 and -`uv_build`. Strict Twine checks validate both artifacts' metadata and README -rendering. Both OSes check artifact contents, matching wheel/sdist package bytes, -and an isolated base-wheel install. Ubuntu and Windows install that exact wheel -and run the full test suite; lint and type checks must also succeed. Tests run -from the runner's temporary directory so CatBoost writes stay outside the checkout. - -The artifact is immutable within the run. `publish` waits for all reusable CI -jobs and `pypi_env` approval, downloads `distributions` and uploads those files without -rebuilding or checking out source. Only this job has `id-token: write`; the -workflow otherwise permits only `contents: read`. Trusted Publishing exchanges -OIDC identity for short-lived PyPI credentials through the documented -[PyPA publish action](https://github.com/pypa/gh-action-pypi-publish). - -Publication and OIDC authorization cannot be tested locally. The first authorized -upload is the verification of the registered publisher and approval gate. +## Release flow + +1. Set `project.version` in `pyproject.toml` to a new version in a reviewed + change, then merge it into `main` through the normal integration process. +2. `publish.yml` checks that the push is to `main`, that the version changed + from the previous commit, and that this exact version is not already on + PyPI. Unchanged versions skip publication; an existing version, network + failure or unexpected PyPI response fails the workflow. +3. The workflow runs the full reusable CI: Python packaging, lint, type checks, + tests on Ubuntu and Windows, and all frontend checks, tests and builds. +4. If every check passes, Actions uploads the exact CI-built wheel and source + archive to [mlbundle on PyPI](https://pypi.org/project/mlbundle/). + Install with `python -m pip install "mlbundle[sklearn,schema]==0.2.0"` on + Python 3.14, replacing the example version as needed. + +The GitHub `pypi_env` environment contains the project-scoped +`MLBUNDLE_API_TOKEN` Actions secret and allows deployments from `main` only. +The workflow uses it as the PyPI token; the token value must never appear in +the repository or logs. There is no manual approval gate, tag, GitHub release +or alternate publish workflow. + +## Checks and recovery + +`publish.yml` uses a read-only preflight before calling `ci.yml`. It skips when +the version has not changed and rejects versions already on PyPI. The reusable +CI checks the same version again before building, so a release that appears +during the run blocks publication. One concurrency group prevents overlapping +publish runs. + +The CI artifact is immutable within the run. `publish` waits for all reusable +CI jobs, downloads the `distributions` artifact and uploads those files without +rebuilding or checking out source. The workflow has `contents: read`; only the +publish job receives the environment-scoped token. Website deployment is +skipped here and follows its own automatic path after successful CI on `main`, +described in [website deployment](deployment.md). + +If upload fails after PyPI accepted one artifact, inspect the version on PyPI +before retrying. The preflight refuses any existing version, and the publish +action does not skip duplicate files. diff --git a/docs/src/components/BrandHead.astro b/docs/src/components/BrandHead.astro new file mode 100644 index 0000000..2d4b9f8 --- /dev/null +++ b/docs/src/components/BrandHead.astro @@ -0,0 +1,19 @@ +--- +import { getImage } from "astro:assets"; +import mark from "../../../brand/mlbundle.png"; +import monaLicense from "../fonts/mona-sans-LICENSE.txt?url"; +import monoLicense from "../fonts/jetbrains-mono-LICENSE.txt?url"; + +const favicon = await getImage({ src: mark, width: 64, format: "png" }); +const { head } = Astro.locals.starlightRoute; +const tags = head.filter( + ({ tag, attrs }) => !(tag === "link" && String(attrs?.rel ?? "").split(/\s+/).includes("icon")), +); +--- + +{tags.map(({ tag: Tag, attrs, content }) => )} + + + + + diff --git a/docs/src/components/BrandTitle.astro b/docs/src/components/BrandTitle.astro new file mode 100644 index 0000000..428a9df --- /dev/null +++ b/docs/src/components/BrandTitle.astro @@ -0,0 +1,54 @@ +--- +import { Image } from "astro:assets"; +import mark from "../../../brand/mlbundle.png"; +const { siteTitleHref } = Astro.locals.starlightRoute; +--- + + + + + + MLbundle + + + by eshkra + + + diff --git a/docs/src/components/Footer.astro b/docs/src/components/Footer.astro new file mode 100644 index 0000000..f458194 --- /dev/null +++ b/docs/src/components/Footer.astro @@ -0,0 +1,41 @@ +--- +import Default from "@astrojs/starlight/components/Footer.astro"; +--- + + +
+ mlbundle is part of eshkra, open-source tools for models people + can use. + + GitHub + PyPI + MIT license + +
+ + diff --git a/docs/src/components/SocialIcons.astro b/docs/src/components/SocialIcons.astro new file mode 100644 index 0000000..fda8ea4 --- /dev/null +++ b/docs/src/components/SocialIcons.astro @@ -0,0 +1,54 @@ +--- +import { Icon } from "@astrojs/starlight/components"; +import { landingUrl, playgroundUrl } from "../sites"; +--- + +Product +PyPI + + mlbundle on GitHub + + + + Playground + + + diff --git a/docs/src/components/SpecPage.astro b/docs/src/components/SpecPage.astro new file mode 100644 index 0000000..a34a029 --- /dev/null +++ b/docs/src/components/SpecPage.astro @@ -0,0 +1,31 @@ +--- +import StarlightPage from "@astrojs/starlight/components/StarlightPage.astro"; +import { getEntry, render } from "astro:content"; +import { specPages } from "../spec"; + +interface Props { + file: (typeof specPages)[number]["file"]; +} + +const { file } = Astro.props; +const entry = await getEntry("spec", file); +if (!entry) throw new Error(`Missing spec source ${file}`); +const { Content, headings } = await render(entry); +const page = specPages.find((spec) => spec.file === file)!; +--- + + depth > 1)} +> +

+ Normative source: {file}. This page renders that file unchanged; the guides explain it, this text defines it. +

+ + diff --git a/docs/src/content.config.ts b/docs/src/content.config.ts new file mode 100644 index 0000000..8cb3dd4 --- /dev/null +++ b/docs/src/content.config.ts @@ -0,0 +1,33 @@ +import { readFile } from "node:fs/promises"; +import { defineCollection } from "astro:content"; +import { z } from "astro/zod"; +import type { Loader } from "astro/loaders"; +import { docsLoader } from "@astrojs/starlight/loaders"; +import { docsSchema } from "@astrojs/starlight/schema"; +import { specMarkdown, specPages, specTitle } from "./spec"; + +const root = new URL("../../", import.meta.url); + +// The normative format and security files, read in place from the repository root on every build. +const specLoader: Loader = { + name: "mlbundle-spec", + load: async ({ store, renderMarkdown, watcher }) => { + store.clear(); + for (const { file, label } of specPages) { + const url = new URL(file, root); + const body = await readFile(url, "utf8"); + store.set({ + id: file, + data: { title: specTitle(body, label) }, + body, + rendered: await renderMarkdown(specMarkdown(file, body), { fileURL: url }), + }); + } + watcher?.add(specPages.map(({ file }) => new URL(file, root).pathname)); + }, +}; + +export const collections = { + docs: defineCollection({ loader: docsLoader(), schema: docsSchema() }), + spec: defineCollection({ loader: specLoader, schema: z.object({ title: z.string() }) }), +}; diff --git a/docs/src/content/docs/adapters/catboost.md b/docs/src/content/docs/adapters/catboost.md new file mode 100644 index 0000000..49bf4a8 --- /dev/null +++ b/docs/src/content/docs/adapters/catboost.md @@ -0,0 +1,52 @@ +--- +title: CatBoost +description: Bundle numeric, single-target symmetric CatBoost models as native CBM with complete, verified tree structure. +--- + +Install `mlbundle[catboost,schema]` (CatBoost 1.2.10 or newer; it brings matplotlib and plotly). +Models are stored as native CBM and loaded in memory. + +```python +from catboost import CatBoostClassifier + +from mlbundle import export, load + +rows = [[0.0, 1.0], [1.0, 0.0], [0.1, 0.9], [0.9, 0.1]] +model = CatBoostClassifier( + iterations=3, depth=2, verbose=False, allow_writing_files=False +) +model.fit(rows, ["cold", "warm"] * 2) +export(model, "catboost.mlbundle") + +bundle = load("catboost.mlbundle") +print(bundle.class_labels) # [['cold', 'warm']] +assert bundle.predict_proba(rows) == model.predict_proba(rows).tolist() +``` + +CatBoost owns its labels: `classes_` is verified on load and `class_labels=` is rejected, even when +equal. + +## Supported + +- Exact fitted `CatBoostClassifier` and `CatBoostRegressor`, one target, numeric features. +- `SymmetricTree` (oblivious trees) only. +- Classifier losses `Logloss`, `CrossEntropy` and `MultiClass`. +- Regressor losses `RMSE`, `MAE`, `Quantile`, `Huber`, `Poisson` and `MAPE`, including their + native parameter strings. + +Complete trees, feature borders, NaN handling, scale and bias, and feature importances are carried +in `structure.json` and verified on load. `predicted_label` uses CatBoost's own class prediction, +including its threshold. + +## Rejected + +`class_labels=`; categorical, text and embedding features; `Depthwise` and `Lossguide` growth; +`MultiClassOneVsAll` and other losses; multi-target and uncertainty models; rankers, bare +`CatBoost`, subclasses; custom Python objectives or metrics; unknown wrapper attributes. + +Categorical features round-trip natively in CBM, but their CTR storage has an unchecked native +loader, so mlbundle rejects them before export and again before native load. Encode categories +yourself, or use a scikit-learn prefix in a [composite pipeline](/adapters/pipelines/). + +The wrapper recovery, native-parser limits and in-memory serializer are specified in +[CatBoost mapping](/format/catboost/). diff --git a/docs/src/content/docs/adapters/onnx.md b/docs/src/content/docs/adapters/onnx.md new file mode 100644 index 0000000..c442c19 --- /dev/null +++ b/docs/src/content/docs/adapters/onnx.md @@ -0,0 +1,59 @@ +--- +title: ONNX +description: Bundle an existing self-contained tabular ONNX file unchanged, with verified signatures, labels and a CPU session on load. +--- + +Install `mlbundle[onnx,schema]`. mlbundle does not convert models to ONNX. It bundles an ONNX file +you already have, byte for byte, after validating that it is a self-contained tabular model. + +This example assumes `trained.onnx` is a classifier with two numeric features and two probability +columns, and no label output: + +```python +from pathlib import Path + +from mlbundle import export, load + +original = Path("trained.onnx").read_bytes() +export("trained.onnx", "onnx.mlbundle", class_labels=[["cold", "warm"]]) + +bundle = load("onnx.mlbundle") +assert bundle.native_model.data == original +assert bundle.native_model.session.get_providers() == ["CPUExecutionProvider"] +print(bundle.class_labels) # [['cold', 'warm']] +print(bundle.predict_proba([[0.0, 0.0]])) +``` + +`export` accepts a path, bytes or an `onnx.ModelProto` (serialized once). `native_model` holds the +original `data` and an onnxruntime `session` on the CPU execution provider. + +## Labels + +- Labels in the graph (`ZipMap`, `LinearClassifier`, `TreeEnsembleClassifier` or `SVMClassifier` + attributes) are verified; supplied `class_labels=` must agree with them. +- Without graph labels, supplied labels are declarations checked only for count; without either, + encoded indices `0..N-1` are used. +- `predicted_label` comes from the graph's label output. Without one it is `None`; mlbundle never + computes an argmax. + +## Supported graphs + +- Rank-two numeric inputs (`float32`, `float64`, `int32`, `int64`) with a fixed width from 1 to + 100,000, or several width-one inputs whose names become verified feature names. +- A batch dimension that is unknown, symbolic or exactly one. +- One target: a regression output of shape `[batch]` or `[batch, 1]`, or a probability matrix or + ZipMap sequence, optionally with a label tensor. + +Kind is determined from the graph's outputs, never declared. Verified claims include input and +output signatures, opsets, IR version, producer, model metadata properties and native labels. +ONNX has no visualization structure in version 1. + +## Rejected before any session + +External tensor data, custom operator domains, model-local functions, runtime or training +configuration, image, string or sequence inputs, multi-target regression, fixed batches above one +and outputs whose kind cannot be determined. Validation scans the protobuf with bounded work before +onnxruntime sees it. + +Tested with ONNX 1.23.0 and onnxruntime 1.30.0. The exact layouts and bounds are in +[ONNX mapping](/format/onnx/). diff --git a/docs/src/content/docs/adapters/overview.md b/docs/src/content/docs/adapters/overview.md new file mode 100644 index 0000000..8c9d80b --- /dev/null +++ b/docs/src/content/docs/adapters/overview.md @@ -0,0 +1,39 @@ +--- +title: Supported models +description: The four model libraries mlbundle supports, how each is stored, what structure it carries and where the support boundary lies. +--- + +mlbundle supports four libraries through closed adapters, plus scikit-learn pipelines that end in +XGBoost or CatBoost. There is no conversion between formats and no generic fallback: a model +outside an adapter's boundary fails on export, on the training machine. + +| Library | Extra | Stored as | Bundle member | Structure | Class labels | +| --- | --- | --- | --- | --- | --- | +| [scikit-learn](/adapters/sklearn/) | `sklearn` | skops, closed allowlist | `model.skops` | Trees, ensembles, linear, importances; `null` for other families | Native `classes_` | +| [XGBoost](/adapters/xgboost/) | `xgboost` | Native UBJSON | `model.ubj` | Complete JSON tree dumps | Supplied labels bound to a native copy, or encoded indices | +| [CatBoost](/adapters/catboost/) | `catboost` | Native CBM | `model.cbm` | Complete symmetric trees and borders | Native `classes_` | +| [ONNX](/adapters/onnx/) | `onnx` | Your file, byte for byte | `model.onnx` | `null` | Graph labels, or count-checked declarations | +| [Pipelines](/adapters/pipelines/) | `sklearn` + native | skops prefix + native leaf | `model.skops` + `model.ubj`/`model.cbm` | Leaf structure with the transformed boundary | From the final estimator | + +Every adapter owns recognition, export preparation, serialization, loading, claim and structure +extraction, and prediction. The same extraction code runs on export and on load, so the format is +defined once. + +## Shared limits + +- XGBoost, CatBoost and ONNX models have exactly one output. scikit-learn supports up to 1,024. +- At most 100,000 features and 100,000 classes per output. +- Classifiers must produce probabilities; there is no decision-only classifier support. + +## Explicitly out of scope + +mlbundle does not support: + +- other libraries, including LightGBM and PyTorch; +- converting scikit-learn models to ONNX, or any other format conversion; +- per-model environments and bundle signing; +- pickle, joblib or cloudpickle, even as an option. + +The normative mappings are in [Adapters and structure](/format/adapters/), +[Prediction state](/format/state/), [CatBoost mapping](/format/catboost/), +[ONNX mapping](/format/onnx/) and [Composite pipelines](/format/pipeline/). diff --git a/docs/src/content/docs/adapters/pipelines.md b/docs/src/content/docs/adapters/pipelines.md new file mode 100644 index 0000000..3140d73 --- /dev/null +++ b/docs/src/content/docs/adapters/pipelines.md @@ -0,0 +1,66 @@ +--- +title: Composite pipelines +description: Bundle a fitted scikit-learn Pipeline that ends in XGBoost or CatBoost, storing the prefix with skops and the final estimator natively. +--- + +A fitted `sklearn.pipeline.Pipeline` whose final step is an exact XGBoost or CatBoost wrapper is +exported as it is. The transformer prefix is stored with skops, the final estimator with its native +adapter, and load rebuilds a real `Pipeline` after verifying both parts. Install +`mlbundle[sklearn,xgboost,schema]` or `mlbundle[sklearn,catboost,schema]`. + +```python +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler +from xgboost import XGBClassifier + +from mlbundle import export, load + +rows = [[0.0, 1.0], [1.0, 0.0], [0.1, 0.9], [0.9, 0.1]] +model = make_pipeline(StandardScaler(), XGBClassifier(n_estimators=3, n_jobs=1)) +model.fit(rows, [0, 1, 0, 1]) +export(model, "pipeline.mlbundle", class_labels=[["cold", "warm"]]) + +bundle = load("pipeline.mlbundle") +assert bundle.predict_proba(rows) == model.predict_proba(rows).tolist() +assert bundle.native_model.predict(rows).tolist() == model.predict(rows).tolist() +print([member.value for member in bundle.members]) +# ['manifest.json', 'model.skops', 'model.ubj', 'schema.json', 'structure.json'] +``` + +## How it is stored + +| Part | Member | Verified on load | +| --- | --- | --- | +| Transformer prefix | `model.skops` | Step names, order, options and a fitted-state commitment | +| Final XGBoost estimator | `model.ubj` | The full XGBoost claims and tree structure | +| Final CatBoost estimator | `model.cbm` | The full CatBoost claims and tree structure | + +A single-step pipeline stores only the native leaf. The manifest reports the library as `sklearn` +with serialization `pipeline`, and `model.pipeline` records the steps, the final estimator's claims +and the **transformed-feature boundary** between them. All-scikit-learn pipelines are not composite: +they use one skops member and have no structure. + +## Labels, features and structure + +- Labels follow the final estimator: XGBoost uses the labels you bind with `class_labels=`, and its + native `classes_`/`predict` stay encoded; CatBoost keeps its native labels. +- Manifest features, schema, training dtypes and samples describe the **original inputs**. +- Trees address the **transformed features**, without attribution back to the original columns. + `mlbundle inspect` prints the boundary's width and names. +- Six members are enough for manifest, prefix, leaf, structure, schema and explicit samples. + +## Supported prefixes + +Scalers, `PCA`, `SimpleImputer` (with indicators and empty columns), `KBinsDiscretizer`, +`PolynomialFeatures`, univariate selectors, `OneHotEncoder`, `OrdinalEncoder`, identity or closed +NumPy `FunctionTransformer`, nested native-free `Pipeline` and `ColumnTransformer`. Each needs a +fitted, data-free proof of its output width. + +## Rejected + +Transformers whose width cannot be proven, `FeatureUnion`, `KNNImputer`, slice, callable or scalar +column selectors, pipeline caching (`memory`), metadata routing, and native estimators anywhere +other than the final step. + +Step names and outer options are reconstruction declarations; no name from the manifest ever +selects loading code. The full representation is in [Composite pipelines](/format/pipeline/). diff --git a/docs/src/content/docs/adapters/sklearn.md b/docs/src/content/docs/adapters/sklearn.md new file mode 100644 index 0000000..44cf8f6 --- /dev/null +++ b/docs/src/content/docs/adapters/sklearn.md @@ -0,0 +1,83 @@ +--- +title: scikit-learn +description: Which scikit-learn estimators and transformers mlbundle can bundle with skops, what structure they carry and what is rejected. +--- + +Install `mlbundle[sklearn,schema]`. Models are stored with `skops.io.dumps` and loaded with +`skops.io.loads` against a trusted-types allowlist that lives inside mlbundle. The allowlist is +never read from a bundle, and you cannot extend it. + +```python +from sklearn.ensemble import RandomForestRegressor + +from mlbundle import export, load + +rows = [[0.0, 1.0], [1.0, 0.0], [0.1, 0.9], [0.9, 0.1]] +model = RandomForestRegressor(n_estimators=3, random_state=0).fit( + rows, [1.0, 2.0, 1.1, 1.9] +) +export(model, "forest.mlbundle", feature_names=["temperature", "humidity"]) + +bundle = load("forest.mlbundle") +assert bundle.predict(rows) == model.predict(rows).tolist() +structure = bundle.structure +print(structure["family"], structure["estimator_shape"]) # tree_ensemble [3] +``` + +## Supported estimators + +Fitted classifiers that expose `predict_proba`, and regressors whose feature and output counts can +be recovered. Tested families include: + +- decision trees, random forests, extra trees and gradient boosting; +- linear, regularized, robust and generalized linear models; +- SVMs, `PLSRegression`, naive Bayes, LDA and QDA; +- dense brute-force neighbors and `NearestCentroid`; +- `MLPClassifier`/`MLPRegressor`; +- `MultiOutputRegressor`/`MultiOutputClassifier` and `RegressorChain`. + +Transformers that round-trip, including inside nested `ColumnTransformer` pipelines: +`SimpleImputer`, `KNNImputer`, scalers, `KBinsDiscretizer`, categorical encoders, univariate +selectors and `FunctionTransformer` with closed NumPy functions. + +The exact class, attribute and function lists are in [Prediction state](/format/state/); the +tested configurations are in +[`tests/test_sklearn_families.py`](https://github.com/UlloaSP/mlbundle/blob/main/tests/test_sklearn_families.py). + +## Structure + +| Model | `structure.json` family | +| --- | --- | +| Single tree (`tree_`) | `decision_tree` | +| Forests, extra trees, gradient boosting | `tree_ensemble` | +| Models with `coef_`/`intercept_` | `linear` | +| Models with only `feature_importances_` | `feature_importances` | +| All-scikit-learn `Pipeline`, other families | `null` | + +scikit-learn structures also carry a `prediction_state` commitment and `state_sha256`, so the +fitted state used for prediction is bound to the bundle. An all-scikit-learn pipeline has no +structure, because version 1 has no mapping from its final step back to the input features, but +its claims and state commitment still cover every step. + +## Rejected + +Export fails with `UnsupportedModelError`, listing the untrusted types where skops finds them: + +- custom estimators and subclasses; +- functions and types outside the closed lists, including lambdas and your own functions; +- compound fitted dtypes; +- classifiers without `predict_proba`; +- multilabel and `ClassifierChain` layouts; +- untrusted nested storage, such as fitted sparse neighbor arrays; +- `HistGradientBoostingClassifier`/`Regressor`, whose `TreePredictor` storage is not on the + allowlist. + +You cannot opt in to trusting a rejected type. Rewrite the model with supported components, or keep +it out of the bundle. + +## Versions + +The allowlist is audited and tested against **scikit-learn 1.9.1 with skops 0.16.0**, not every +version the `>=1.6` range allows. Unknown fitted state fails. Use the same scikit-learn version for +export and load; a failed load under a different declared version raises +`EnvironmentMismatchError` to say so. diff --git a/docs/src/content/docs/adapters/xgboost.md b/docs/src/content/docs/adapters/xgboost.md new file mode 100644 index 0000000..81ed4ab --- /dev/null +++ b/docs/src/content/docs/adapters/xgboost.md @@ -0,0 +1,60 @@ +--- +title: XGBoost +description: Bundle XGBClassifier and XGBRegressor as native UBJSON, bind original class labels and know which settings survive. +--- + +Install `mlbundle[xgboost,schema]`. Models are stored as native in-memory UBJSON and loaded with +`load_model`, never pickled. + +```python +from xgboost import XGBClassifier + +from mlbundle import export, load + +rows = [[0.0, 1.0], [1.0, 0.0], [0.1, 0.9], [0.9, 0.1]] +model = XGBClassifier(n_estimators=3, max_depth=2, n_jobs=1).fit(rows, [0, 1, 0, 1]) +export(model, "xgboost.mlbundle", class_labels=[["cold", "warm"]]) + +bundle = load("xgboost.mlbundle") +assert isinstance(bundle.native_model, XGBClassifier) +assert bundle.predict_proba(rows) == model.predict_proba(rows).tolist() +assert bundle.native_model.predict(rows).tolist() == model.predict(rows).tolist() +print(bundle.class_labels) # [['cold', 'warm']] +``` + +## Class labels + +XGBoost trains on encoded indices. Supply the original labels in probability-column order, nested +even for one output. mlbundle binds them, with the prediction settings, to an attribute on a +**copy** of the booster; your model object is not changed. Load verifies that the binding is +consistent, not how the model was trained. + +- Without `class_labels=`, encoded indices are used, or an existing binding. +- The native model's `predict` keeps returning encoded indices; `predicted_label` in + [records](/guides/records/) maps the decision to your label. +- Regressors reject `class_labels=`. + +## Supported + +- Exact `XGBClassifier` and `XGBRegressor`, one target. +- Native `gbtree` storage, including XGBoost 3.4.1's `dart` option, which uses `gbtree` storage. +- Classifier objectives `binary:logistic` and `multi:softprob`. +- Regression objectives `reg:squarederror`, `reg:absoluteerror`, `reg:pseudohubererror`, + `reg:squaredlogerror`, `reg:logistic`, `count:poisson`, `reg:gamma` and `reg:tweedie`. +- At most 100,000 trees and depth 14. + +Structure is the complete list of JSON tree dumps with statistics, plus the native model record. + +## Rejected + +Raw `Booster` objects, `XGBRFClassifier`/`XGBRFRegressor`, subclasses, ranking, survival and custom +objectives, softmax-only objectives, `gblinear`, legacy `dart` storage and multiple targets. + +## What survives + +Only configuration that native XGBoost I/O preserves is recorded and verified: the learner model +parameters, objective, booster, the bound prediction settings (`missing`, `importance_type`, +`enable_categorical`) and best-iteration attributes. Learning rate, max depth, seed and thread count +do not survive native I/O and are neither recorded nor reconstructed. + +The exact mapping is in [Implemented XGBoost mapping](/format/adapters/#implemented-xgboost-mapping). diff --git a/docs/src/content/docs/getting-started/installation.md b/docs/src/content/docs/getting-started/installation.md new file mode 100644 index 0000000..17bd87e --- /dev/null +++ b/docs/src/content/docs/getting-started/installation.md @@ -0,0 +1,87 @@ +--- +title: Installation +description: Install mlbundle 0.2.0 with the extras for your model library, from PyPI or a local checkout, and check the Python and dependency ranges. +--- + +mlbundle requires **Python 3.14 or newer**. Its only core dependency is Pydantic v2; every model +library is an optional extra that is imported lazily, only when a bundle needs it. + +These pages document mlbundle 0.2.0. + +```bash +python -m pip install "mlbundle[sklearn,schema]==0.2.0" +``` + +Replace `sklearn` with the extra for your model library; the extras are listed +[below](#choose-the-extras). + +## From a local checkout + +To work from source, or before 0.2.0 is available from your package index, install a checkout of +the repository: + +```bash +git clone https://github.com/UlloaSP/mlbundle.git +cd mlbundle +python -m pip install ".[sklearn,schema]" +``` + +Inside a uv project, add the checkout as a path dependency instead (adjust the path to where you +cloned it): + +```bash +uv add ../mlbundle --extra sklearn --extra schema +``` + +## Choose the extras + +| Extra | Installs | Needed for | +| --- | --- | --- | +| `sklearn` | `scikit-learn>=1.6`, `skops>=0.16.0` | Exporting and loading scikit-learn models and pipeline prefixes. | +| `xgboost` | `xgboost>=3.4.1`, `scikit-learn>=1.2` | `XGBClassifier` and `XGBRegressor`. | +| `catboost` | `catboost>=1.2.10` | CatBoost classifiers and regressors. CatBoost brings matplotlib and plotly. | +| `onnx` | `onnx>=1.23.0`, `onnxruntime>=1.30.0` | Existing ONNX files. | +| `schema` | `mlschema>=0.3.0,<0.4` on Python 3.14 only | **Every export.** Also installs pandas. | +| `all` | All of the above | Development and mixed workloads. | + +Pick by what each machine does: + +- **Training machine:** the model extra plus `schema`, for example `mlbundle[xgboost,schema]`. + Export always infers a schema, even without training data. +- **Serving or verifying machine:** only the matching model extra, for example `mlbundle[xgboost]`. + Loading validates the schema without pandas or mlschema. +- **Metadata only:** no extra. `inspect` and `mlbundle inspect` never import a model library. + +Composite pipelines need both parts: `[sklearn,xgboost]` or `[sklearn,catboost]`. + +:::caution[Schema inference is Python 3.14 only] +The `schema` extra is limited to Python 3.14 because mlschema supports only that version. Export +therefore runs on 3.14. Inspecting and loading validate the schema without mlschema. +::: + +## Verify the installation + +```python +from importlib.metadata import version + +import mlbundle + +print(version("mlbundle")) # 0.2.0 +print(mlbundle.FORMAT_NAME, mlbundle.FORMAT_VERSION) # mlbundle 1 +``` + +The console script is installed with the package: + +```bash +mlbundle --help +``` + +## Troubleshooting + +| Symptom | Cause | Resolution | +| --- | --- | --- | +| `MissingOptionalDependencyError` naming `schema` | Export runs without pandas or mlschema. | Install `mlbundle[schema]` on Python 3.14. | +| `MissingOptionalDependencyError` naming a library | Load or `verify` needs the model's extra. | Install the extra named in the message. | +| `mlbundle: missing optional dependency; install mlbundle[xgboost].` | Same, from the CLI. | Install that extra; `inspect` works without it. | +| `EnvironmentMismatchError` on load | A skops load failed and the bundle was written with a different scikit-learn version. | Match the training scikit-learn version. | +| `No matching distribution found` | Python is older than 3.14. | Check `python --version`. | diff --git a/docs/src/content/docs/getting-started/quick-start.md b/docs/src/content/docs/getting-started/quick-start.md new file mode 100644 index 0000000..f407d63 --- /dev/null +++ b/docs/src/content/docs/getting-started/quick-start.md @@ -0,0 +1,100 @@ +--- +title: Quick start +description: Export a scikit-learn classifier to a .mlbundle file, inspect its metadata, load the verified model and predict. +--- + +This walkthrough uses scikit-learn. Install `mlbundle[sklearn,schema]` on Python 3.14 as described +in [Installation](/getting-started/installation/). + +## 1. Export on the training machine + +```python title="train.py" +from sklearn.tree import DecisionTreeClassifier + +from mlbundle import export + +rows = [[0.0, 1.0], [1.0, 0.0], [0.1, 0.9], [0.9, 0.1]] +model = DecisionTreeClassifier(random_state=7).fit(rows, ["cold", "warm"] * 2) + +view = export(model, "trained.mlbundle", feature_names=["temperature", "humidity"]) +print([member.value for member in view.members]) +# ['manifest.json', 'model.skops', 'schema.json', 'structure.json'] +``` + +`export` serializes the model with skops, loads it back, recomputes its claims and structure, and +only then publishes the file. A model with types outside mlbundle's allowlist fails here, on the +training machine, not after upload. An existing destination needs `overwrite=True`. + +The model was fitted on plain lists, so it has no native feature names: the names you pass are +recorded as `declared`. No training data was given, so the schema comes from one synthetic row. + +## 2. Inspect without loading the model + +```python +from mlbundle import inspect + +view = inspect("trained.mlbundle") +print(view.manifest.model.library, view.manifest.model.estimator_class) +# sklearn DecisionTreeClassifier +print(view.manifest.features.names, view.manifest.features.origin) +# ['temperature', 'humidity'] declared +print(view.schema["reports"][0]["labels"]) # ['cold', 'warm'] +``` + +`inspect` validates the archive, the manifest, every non-manifest member's hash and the schema, without importing +scikit-learn or deserializing anything. It therefore **cannot** tell you whether the metadata +describes the model. The command line does the same: + +```bash +mlbundle inspect trained.mlbundle +``` + +## 3. Load and predict + +```python +from mlbundle import load + +bundle = load("trained.mlbundle") +print(bundle.kind, bundle.class_name, bundle.class_labels) +# classifier DecisionTreeClassifier [['cold', 'warm']] +print(bundle.predict_proba([[0.0, 1.0]])) # [[1.0, 0.0]] +``` + +`load` performs every `inspect` check, deserializes through the scikit-learn adapter, recomputes +the hyperparameters, counts, labels and tree, and raises `ClaimMismatchError` if any of them +differs from the manifest or `structure.json`. Classifiers use `predict_proba`, regressors +`predict`; both return plain lists. Probabilities are output-major: one matrix per output, which +for a single output is the matrix itself. + +`bundle.native_model` is the real `DecisionTreeClassifier`, for native inference or explanations. +Mutating it invalidates what was verified. + +## 4. Predict from a record + +Forms and APIs send values keyed by name. Records use the schema's mappings: positions for this +model, because its names were declared rather than native. + +```python +result = bundle.predict_record({"0": "0.0", "1": "true"})[0] +print(result.labels, result.probabilities, result.predicted_label) +# ('cold', 'warm') (1.0, 0.0) cold +``` + +Strings are parsed strictly, every feature is required, and `predicted_label` is the model's own +class decision. See [Records and predictions](/guides/records/). + +## 5. Verify from the command line + +```bash +mlbundle verify trained.mlbundle +``` + +```text +Valid bundle: sklearn "DecisionTreeClassifier"; verified format, integrity, schema/samples and native model claims/structure. +``` + +## Next steps + +- Use your real training frame for dtypes and categories: [Schema and samples](/guides/schema-and-samples/). +- Bundle XGBoost, CatBoost, ONNX or a pipeline: [Supported models](/adapters/overview/). +- Understand what each check proves: [What is verified](/guides/trust/). diff --git a/docs/src/content/docs/guides/cli.md b/docs/src/content/docs/guides/cli.md new file mode 100644 index 0000000..14e94c3 --- /dev/null +++ b/docs/src/content/docs/guides/cli.md @@ -0,0 +1,73 @@ +--- +title: Command line +description: The mlbundle inspect and verify commands, their output, JSON mode and exit codes. +--- + +Installing mlbundle adds an `mlbundle` console script with two commands. Both use the same readers +as the Python API. + +```bash +mlbundle inspect trained.mlbundle +mlbundle inspect trained.mlbundle --json +mlbundle verify trained.mlbundle +``` + +## `inspect` + +Validates metadata and integrity without importing any model library, so it works with the base +install. The first line says what was and was not checked: + +```text +Metadata and integrity validated; model claims NOT verified. +Format: mlbundle 1 +Bundle id: aa6f6b39-574a-499b-a0ff-de4cdd334050 +Created: 2026-10-19T11:17:51.949658+00:00 +Writer: "0.2.0" +Model: sklearn "DecisionTreeClassifier" (classifier) +Counts: features=2, outputs=1, classes=[2] +Features (declared): ["temperature", "humidity"] +Class labels (output-major): [["cold", "warm"]] +Target names (output-major): null +Members: + manifest.json: validated (no self-integrity record) + model.skops: 13065 bytes sha256=24d7cbab1d775ab279dc8ebc3c762b551b397ebf9b4917ac3c4474ff611b8813 + schema.json: 361 bytes sha256=ea62a6a7502ae8ea30b45b61bb18d8e31bd2ec55f026d9fbf0c05ef42d01ff25 + structure.json: 526 bytes sha256=66204da26781a4e2a800a1f3ed246ab1a43e8c97ced73d06a2640f897eeda03c +Training: {"python": "3.14.7", "implementation": "CPython", "os": "Windows", "os_version": "11", "architecture": "AMD64"} +Packages: {"catboost": "1.2.10", "mlbundle": "0.2.0", "mlschema": "0.3.0", ...} +ONNX environment: null +Present: schema=yes, structure=yes, samples=no +User metadata: {} +``` + +Composite pipelines add their step names, final estimator and transformed-feature boundary. +Values in the human summary escape terminal control characters and are truncated at 512 characters, +so a hostile bundle cannot rewrite your terminal. + +`--json` prints the full validated manifest instead: lossless, ASCII-escaped JSON, suitable for +`jq` or archiving. + +## `verify` + +Loads the bundle through its adapter and recomputes every model-derived claim and the structure. It +needs the model's extra: + +```text +Valid bundle: sklearn "DecisionTreeClassifier"; verified format, integrity, schema/samples and native model claims/structure. +``` + +## Errors and exit codes + +Errors are a single bounded line on stderr naming the failure category. They never print bundle +content or a traceback. + +| Exit code | Meaning | Example stderr | +| --- | --- | --- | +| `0` | Success. | | +| `1` | The bundle was rejected, or a dependency is missing. | `mlbundle: InvalidContainerError: bundle rejected.` | +| `1` | | `mlbundle: missing optional dependency; install mlbundle[xgboost].` | +| `2` | Usage or I/O failure. | `mlbundle: FileNotFoundError: I/O failed.` | +| `2` | | `mlbundle: invalid arguments; use --help.` | + +Use `verify` in CI or before publishing a bundle elsewhere; use `inspect` where model libraries are +not installed or should not run. diff --git a/docs/src/content/docs/guides/export.md b/docs/src/content/docs/guides/export.md new file mode 100644 index 0000000..47682e8 --- /dev/null +++ b/docs/src/content/docs/guides/export.md @@ -0,0 +1,144 @@ +--- +title: Export a bundle +description: Every export argument, how feature names and class labels are resolved, what the manifest records, and how the file is published. +--- + +`export` runs on the training machine. It rejects unsupported models there, before any file +exists, and publishes a bundle only after it has been read back and verified. + +```python +def export( + model: object, + destination: str | PathLike[str], + *, + training_data: object | None = None, + feature_names: Sequence[str] | None = None, + class_labels: Sequence[Sequence[Label]] | None = None, + target_names: Sequence[str] | None = None, + sample_rows: int | None = None, + onehot_separator: str = "__", + metadata: Mapping[str, JsonValue] | None = None, + overwrite: bool = False, + limits: ArchiveLimits | None = None, +) -> MetadataView +``` + +Export always needs the `schema` extra, even without `training_data`. + +## Arguments + +| Argument | Meaning | +| --- | --- | +| `model` | A fitted, supported model: see [Supported models](/adapters/overview/). For ONNX, a path, bytes or a `ModelProto`. | +| `destination` | Path of the `.mlbundle` file to write. | +| `training_data` | Optional nonempty pandas DataFrame, used locally for schema inference and dtypes. Never stored. | +| `feature_names` | Names for models without native names. Ignored in favour of native names; must match the frame's columns when a frame is given. | +| `class_labels` | Output-major original labels, nested even for one output. For XGBoost, ONNX (verified against graph labels when present, count-checked otherwise) and composites ending in XGBoost. Rejected by scikit-learn, CatBoost and regressors. | +| `target_names` | One unique name per output, in output order. With a frame, these columns are excluded. A bare string is rejected. | +| `sample_rows` | Positive integer: include the first N feature rows as `samples.json`. Requires a frame. See [samples](/guides/schema-and-samples/#explicit-samples). | +| `onehot_separator` | Separator for one-hot groups, 1–16 characters, stored in the schema. | +| `metadata` | Free JSON object recorded as `user_metadata`. | +| `overwrite` | Replace an existing destination. Without it, an existing file raises `FileExistsError`. | +| `limits` | Lower [`ArchiveLimits`](/reference/limits/) for this bundle. Ceilings cannot be raised. | + +`export` returns the same [`MetadataView`](/reference/api/#metadataview) that `inspect` would +produce for the new file. + +## Feature names and origin + +Every bundle records an ordered list of feature names and where they came from: + +| Origin | When | Names | Record keys and schema mappings | +| --- | --- | --- | --- | +| `model` | The model exposes native names, such as `feature_names_in_` after fitting on a DataFrame. | Native names | Names | +| `dataframe` | No native names, `training_data` supplied. | Frame columns | Zero-based positions | +| `declared` | No native names, `feature_names=` supplied. | Your names | Zero-based positions | +| `generated` | None of the above. | `feature_0` … `feature_{N-1}` | Zero-based positions | + +Native names always win. Declared names are labels: they do not add name-based prediction or +reordering. For models without native names, predict with a 2-D NumPy array or nested numeric rows +in manifest feature order. + +```python +import pandas as pd +from sklearn.linear_model import LogisticRegression + +from mlbundle import export + +frame = pd.DataFrame( + {"temperature": [0.0, 1.0, 0.1, 0.9], "humidity": [1.0, 0.0, 0.9, 0.1]} +) +model = LogisticRegression().fit(frame, ["cold", "warm"] * 2) + +view = export(model, "named.mlbundle", training_data=frame, overwrite=True) +print(view.manifest.features.origin, view.manifest.features.names) +# model ['temperature', 'humidity'] +print(view.manifest.features.dtypes) # ['float64', 'float64'] +``` + +## Class labels and targets + +Class labels are **output-major**: a list per output, in probability-column order. Where they come +from depends on the adapter: + +- **scikit-learn and CatBoost** own their `classes_`; `class_labels=` is rejected. +- **XGBoost** is trained on encoded indices. Supply the original labels with + `class_labels=[["cold", "warm"]]`; they are bound to a native copy of the model and verified on + load. Without them, encoded indices are used. See [XGBoost](/adapters/xgboost/). +- **ONNX** labels from the graph are verified; supplied labels must agree. A graph without labels + accepts count-checked declarations. See [ONNX](/adapters/onnx/). +- **Composites** delegate labels to the final native estimator. + +Regressors reject `class_labels=`. `target_names=` is a separate declaration that names each output, +and with a training frame tells mlbundle which columns to exclude from inference, dtypes and samples. + +## What the manifest records + +`manifest.json` holds: + +- identity: format name and version, a random `bundle_id`, `created_at`, `writer_version`; +- model: library, class, kind, hyperparameters, feature/output/class counts and serialization; +- features: ordered names, origin, training dtypes and verified model dtypes; +- target: output-major class labels and target names; +- environment: Python version and implementation, OS and version, architecture and installed + package versions; +- integrity: the SHA-256 and size of every other member; +- `user_metadata`: your `metadata=`, at most 1,024 top-level keys, 64 KiB and depth 8. + +Model-derived values, including model-exposed `model_dtypes`, are claims that `load` recomputes. +The environment, training `dtypes` and user metadata are declarations: recorded, never authenticated. The exact fields are in the +[format 1 manifest](/format/#manifest). + +```python +view = export( + model, + "described.mlbundle", + training_data=frame, + metadata={"name": "weather", "metrics": {"accuracy": 1.0}}, + overwrite=True, +) +print(view.manifest.user_metadata["name"]) # weather +``` + +## Publication + +The writer builds a deterministic ZIP in memory, validates it with the reader, then publishes it +atomically with a hard link or replacement. Where hard links are unavailable it falls back to +exclusive creation, which removes a partial file on failure but exposes the destination while +copying. + +## Export failures + +Validation failures happen before anything is published (with the exclusive-creation fallback, a +failed copy is removed but was briefly visible): + +| Error | Typical cause | +| --- | --- | +| `UnsupportedModelError` | The model class, objective, layout or a contained type is outside the adapter's closed boundary. For scikit-learn the message lists the untrusted types. | +| `InvalidSchemaError` | Frame columns do not match native names, a target column is missing, or report labels collide. | +| `InvalidSamplesError` | `sample_rows` is zero, too large, not an integer, or a cell cannot be converted. | +| `MissingOptionalDependencyError` | The `schema` extra or the model's extra is not installed. | +| `LimitExceededError` | A member or the archive exceeds the selected limits. | +| `FileExistsError` | The destination exists and `overwrite` is false. | + +The full list is in [Errors](/reference/errors/). diff --git a/docs/src/content/docs/guides/inspect-and-load.md b/docs/src/content/docs/guides/inspect-and-load.md new file mode 100644 index 0000000..69ea76c --- /dev/null +++ b/docs/src/content/docs/guides/inspect-and-load.md @@ -0,0 +1,112 @@ +--- +title: Inspect and load +description: Read a bundle's metadata without touching the model, or load it through a closed adapter that recomputes every model-derived claim. +--- + +mlbundle has two readers with different guarantees. Choose by what you need to trust. + +| | `inspect` | `load` | +| --- | --- | --- | +| Archive layout, members, resource limits | Validated | Validated | +| Strict manifest and format version | Validated | Validated | +| SHA-256 and size of every non-manifest member | Verified | Verified | +| Schema and samples against manifest claims | Validated | Validated | +| Imports a model library | **Never** | The matching adapter only | +| Deserializes the model | **Never** | Through the closed adapter | +| Hyperparameters, counts, labels, dtypes, structure | Read as claims | **Recomputed and compared** | +| Returns | `MetadataView` | `LoadedBundle` with model and predictor | + +Both accept a path, `bytes` or a binary stream, and an optional lowered +[`ArchiveLimits`](/reference/limits/). The ZIP is read in memory by allowed member names and never +extracted to disk. + +## `inspect`: metadata only + +```python +from mlbundle import inspect + +view = inspect("trained.mlbundle") +manifest = view.manifest +print(manifest.format_version, manifest.writer_version) # 1 0.2.0 +print(manifest.model.n_features, manifest.model.n_outputs) # 2 1 +print([member.value for member in view.members]) +# ['manifest.json', 'model.skops', 'schema.json', 'structure.json'] +print(view.samples) # None +``` + +`MetadataView` has `manifest`, `members`, `schema` and `samples`. Use it to list, index or review +bundles on a machine without model libraries. + +:::caution[Inspection does not verify the model] +A bundle's author can write any manifest and recompute its hashes. `inspect` checks that the file is +well formed and internally consistent, not that the metadata, labels or structure describe the +model inside. Only `load` (or `mlbundle verify`) establishes that agreement. +::: + +## `load`: a verified model + +```python +from pathlib import Path + +from mlbundle import load + +bundle = load(Path("trained.mlbundle").read_bytes()) # paths, bytes or streams +print(bundle.library, bundle.class_name, bundle.kind) +# sklearn DecisionTreeClassifier classifier +print( + bundle.feature_names, bundle.feature_origin +) # ('temperature', 'humidity') declared +print(bundle.structure["family"]) # decision_tree +``` + +`load` runs every `inspect` check, then: + +1. resolves the adapter from a closed list (`sklearn`, `xgboost`, `catboost`, `onnx`), never from a + class name in the manifest; +2. deserializes with skops against mlbundle's own trusted types, native UBJSON or CBM in memory, or + an ONNX CPU session after graph validation; +3. extracts claims and structure with the same code the writer used and compares them as canonical + JSON bytes, raising `ClaimMismatchError` on any difference. + +### `LoadedBundle` + +| Member | Contents | +| --- | --- | +| `manifest`, `members`, `schema`, `samples` | As in `MetadataView`, validated. | +| `structure` | Decoded `structure.json`, or `None` for families without extractable structure. | +| `native_model` | The native object: estimator, `Pipeline`, XGBoost or CatBoost wrapper, or an ONNX model holding `data` and `session`. | +| `kind`, `library`, `class_name` | From the verified manifest. | +| `feature_names`, `feature_origin` | Ordered names and their origin. | +| `class_labels` | Output-major labels, or `None` for regressors. | +| `predict_proba(data)` | Classifiers only. One probability matrix per output; the matrix itself for one output. | +| `predict(data)` | Regressors only. Row-major values. | +| `row_from_record(record)`, `predict_record(record)` | See [Records and predictions](/guides/records/). | + +Calling `predict_proba` on a regressor, or `predict` on a classifier, raises `ValueError`. Results +are plain Python lists. `native_model` stays available for native inference and explanations; +mutating it invalidates what `load` verified. + +## Structure + +`structure.json` describes the model a reviewer would visualise: decision trees, tree ensembles, +linear coefficients, feature importances, XGBoost tree dumps or CatBoost's symmetric trees. It is +required whenever the adapter can extract it and is compared on load, so a reviewer never sees a +tree that differs from the model used for prediction. All-scikit-learn pipelines and ONNX have +`None`. The families and fields are in [Adapters and structure](/format/adapters/#structurejson). + +## Version diagnostics + +Some failures add a hint without adding a compatibility gate: + +- `EnvironmentMismatchError` when a skops load fails and the bundle declares a different + scikit-learn version than the one installed; +- `InvalidManifestError` mentioning a different writer version when a manifest fails validation. + +Format 1 is not frozen; incompatible changes may require re-export. Bundles written by 0.1.0a1, or +with schema version 1, must be re-exported. + +## Treat every bundle as untrusted + +Validation does not sandbox native parsers or inference. A runtime that accepts uploads still owns +authentication, process isolation and memory, CPU and row limits. See +[What is verified](/guides/trust/) and the [security policy](/security/). diff --git a/docs/src/content/docs/guides/records.md b/docs/src/content/docs/guides/records.md new file mode 100644 index 0000000..8165ff9 --- /dev/null +++ b/docs/src/content/docs/guides/records.md @@ -0,0 +1,126 @@ +--- +title: Records and predictions +description: Predict from arrays or from form-style records keyed by the bundle's schema, and read the frozen per-output results. +--- + +A loaded bundle predicts from two kinds of input: arrays in feature order, and single records keyed +by the schema. Both go through the same verified predictor. + +## Arrays and DataFrames + +```python +from mlbundle import load + +bundle = load("trained.mlbundle") +print(bundle.predict_proba([[0.0, 1.0], [1.0, 0.0]])) # [[1.0, 0.0], [0.0, 1.0]] +``` + +- Classifiers use `predict_proba`; results are **output-major**: one matrix per output, or the + matrix itself when there is one output. +- Regressors use `predict`; results are row-major values. +- For models without native feature names, pass a 2-D NumPy array or nested numeric rows in + manifest feature order. Declared names do not enable name-based selection. +- Adapter-supported DataFrames and sparse arrays keep their native restrictions. + +## Records + +`predict_record` takes one mapping, as a form or JSON API would send it, and returns one frozen +`OutputPrediction` per output. + +```python +import pandas as pd +from sklearn.tree import DecisionTreeClassifier + +from mlbundle import export, load + +frame = pd.DataFrame( + {"temperature": [0.0, 1.0, 0.1, 0.9], "humidity": [1.0, 0.0, 0.9, 0.1]} +) +model = DecisionTreeClassifier(random_state=7).fit(frame, ["cold", "warm"] * 2) +export(model, "named.mlbundle", training_data=frame) + +bundle = load("named.mlbundle") +(result,) = bundle.predict_record( + {"temperature": "0.05", "humidity": 0.95, "note": "ignored"} +) +print(result.labels, result.probabilities, result.predicted_label) +# ('cold', 'warm') (1.0, 0.0) cold +``` + +### Keys + +| Feature origin | Keys | +| --- | --- | +| `model` (native names) | Feature names. | +| `dataframe`, `declared`, `generated` | Zero-based positions, as integers (`0`) or canonical decimal strings (`"0"`). Supplying both forms for one position is an error. | + +Every feature is required. Extra keys are ignored **without iterating the record**, so the work +depends on the feature count, not the size of what a client sent. + +### Values + +- Finite Python `int`, `float` and `bool` values. +- Strings, stripped of ASCII whitespace and at most 64 characters: `true`/`false` in any case, or + decimals such as `42`, `-1.5`, `.5`, `1.` and `2e-3`. +- `None` becomes NaN only when the schema field has `required: false`; for one-hot options the + group's requirement applies. + +Category fields still need the numeric value the model was trained on. Records convert strings to +numbers; they do not encode category names. + +`row_from_record` returns the validated numeric row without predicting, for example to feed an +explainer with the same input: + +```python +print(bundle.row_from_record({"temperature": 0.2, "humidity": " 1e0 "})) # [0.2, 1.0] +``` + +### Errors + +Invalid records raise `PredictionInputError`, which is also a `ValueError`. Messages name the key, +escaped and bounded, but **never echo the value**: + +```text +Prediction record must be a mapping. +Missing features: "humidity". +Feature "temperature": a required feature cannot be None. +Feature "temperature": number must be finite. +Feature "temperature": expected a decimal or true/false string. +Feature 0: both integer and string keys are present. +``` + +Missing-key errors list at most five keys and then a count of the rest. + +## `OutputPrediction` + +```python +@dataclass(frozen=True) +class OutputPrediction: + output: int # report position, in output order + kind: ModelKind # classifier or regressor + labels: tuple[str, ...] | None # report labels, in probability order + probabilities: tuple[float, ...] | None + value: float | None # regression value + predicted_label: str | None +``` + +`predicted_label` is the report label of the **model's own class decision**: scikit-learn +`predict`, the XGBoost wrapper's `predict`, CatBoost's class prediction including its threshold, +or the ONNX graph's label output. It can differ from the label with the largest probability, and +mlbundle never substitutes an argmax. It is `None` for regressors and for ONNX classifiers without +a label output. + +```python +regression = load("reg.mlbundle") # a LinearRegression bundle +print(regression.predict_record({"temperature": 0.5, "humidity": 0.5})) +# (OutputPrediction(output=0, kind=, labels=None, +# probabilities=None, value=2.5, predicted_label=None),) +``` + +## Pipelines that select columns by name + +scikit-learn pipelines whose fitted column selectors use names receive a one-row pandas DataFrame +from `predict_record`; this needs pandas, which the `schema` extra installs. Other models receive a +numeric row, preserving their native categorical handling. + +The normative record rules are in [Runtime record input](/format/schema/#runtime-record-input). diff --git a/docs/src/content/docs/guides/schema-and-samples.md b/docs/src/content/docs/guides/schema-and-samples.md new file mode 100644 index 0000000..577a816 --- /dev/null +++ b/docs/src/content/docs/guides/schema-and-samples.md @@ -0,0 +1,158 @@ +--- +title: Schema and samples +description: How mlbundle infers schema.json locally with mlschema, what it reveals about the training data, and how to include sample rows explicitly. +--- + +Every bundle carries `schema.json`: the input fields a form or API needs, plus one report per +model output. It is inferred **on the training machine** by +[mlschema](https://docs.mlschema.eshkra.dev/). The training DataFrame itself never enters the +bundle. + +## Infer from your training frame + +Pass the frame you trained on. With `target_names`, those columns are excluded from inference, +dtypes and samples; without it, every column must be a feature. + +```python +import pandas as pd +from sklearn.tree import DecisionTreeClassifier + +from mlbundle import export + +training = pd.DataFrame( + { + "age": [34, 51, 29, 46], + "plan": pd.Categorical( + ["basic", "pro", "basic", "team"], + categories=["basic", "pro", "team", "free"], + ), + "color__red": [1, 0, 0, 1], + "color__blue": [0, 1, 1, 0], + "income": [52000.0, None, 61000.0, 48000.0], + "churned": ["no", "yes", "no", "yes"], + } +) +features = training.drop(columns="churned") +encoded = features.assign( + plan=features["plan"].cat.codes, income=features["income"].fillna(0) +) +model = DecisionTreeClassifier(random_state=0).fit( + encoded.to_numpy(), training["churned"] +) + +view = export(model, "churn.mlbundle", training_data=training, target_names=["churned"]) +print(view.manifest.features.origin) # dataframe +print( + view.manifest.features.dtypes +) # ['int64', 'category', 'int64', 'int64', 'float64'] +``` + +The resulting `schema.json`, keys sorted: + +```json +{ + "schema_version": "2", + "source": "training_data", + "onehot_separator": "__", + "fields": [ + { "kind": "number", "label": "age", "mappedTo": 0, "required": true, "step": 1.0 }, + { + "kind": "category", + "label": "plan", + "mappedTo": 1, + "options": ["basic", "pro", "team", "free"], + "required": true + }, + { + "kind": "onehot-category", + "label": "color", + "options": [ + { "label": "red", "mappedTo": 2, "value": "red" }, + { "label": "blue", "mappedTo": 3, "value": "blue" } + ], + "required": true + }, + { "kind": "number", "label": "income", "mappedTo": 4, "required": false, "step": 0.1 } + ], + "reports": [ + { + "kind": "classifier", + "label": "Predicted class", + "labels": ["no", "yes"], + "mappedTo": 0, + "showClassProbabilities": true + } + ] +} +``` + +What happened: + +- `color__red` and `color__blue` are binary columns sharing a prefix before the separator, so they + became one `onehot-category` field. Change the separator with `onehot_separator=` (1–16 + characters); it is stored in the schema. +- `income` has a missing value, so it is `required: false`. +- `plan` keeps its categorical order, **including the unused `free`**. +- The model was fitted on an array, so mappings are zero-based positions. A model with native + feature names maps by name instead. +- The report labels are the string form of the model's classes, in probability-column order. + +Frame columns must be unique strings, in model input order, and must equal native names when the +model has them. `feature_names=` cannot rename or reorder a supplied frame. + +## Without training data + +Without `training_data`, mlschema infers from one synthetic row of `1.0` values, one column per +feature. Fields become required `number` fields with `step: 0.1`, except that named columns sharing +a prefix before the separator can still be grouped as one-hot. `source` is `"default"` and the +training dtypes are `null`: a synthetic row is not evidence about your data. + +## What leaves the training machine + +| Included | Not included | +| --- | --- | +| Feature names and labels | Training rows (unless you request samples) | +| Field kinds and nullability | Numeric or date minima, maxima, means | +| Category values, including unused ones | Row counts | +| One-hot names and suffixes | Example values, defaults, free-text values | +| Dtype strings and dtype-based numeric steps | The DataFrame index | +| Pair-axis names for series fields | | +| Class labels | | + +These declarations can still be sensitive: column and category names reveal what you trained on. +The model, its learned structure and your `metadata=` can independently reveal training +information. Leaving out samples does not prevent model memorization. + +## Explicit samples + +Samples are **real user data**, so they are only ever included when you ask. `sample_rows=N` +includes exactly the first N feature rows of the frame, without target columns or the index. + +```python +from mlbundle import inspect + +export(model, "sampled.mlbundle", training_data=features, sample_rows=2) +print(inspect("sampled.mlbundle").samples) +# [{'age': 34, 'color__blue': 0, 'color__red': 1, 'income': 52000.0, 'plan': 'basic'}, +# {'age': 51, 'color__blue': 1, 'color__red': 0, 'income': None, 'plan': 'pro'}] +``` + +- N must be a positive integer within the frame; `0`, `True` and larger values raise + `InvalidSamplesError`, as does `sample_rows` without `training_data`. +- There is no random sampling or clipping. Shuffle the frame yourself if the first rows are not + representative. +- Missing values become `null`, timestamps ISO 8601 strings, categorical and NumPy scalars plain + JSON values. Bytes, complex values, timedeltas and arbitrary objects are rejected. +- Schema and samples are limited to 16 MiB each and JSON depth 32. + +## Reading schemas + +`inspect` and `load` validate `schema.json` without mlschema or pandas: every feature is covered +exactly once, mappings match the feature origin, and reports match the manifest's class labels. +`load` additionally verifies those labels against the model. The schema's `source`, separator and +nullability remain declarations; nothing can attest how the training data looked. + +Bundles written by 0.1.0a1 without a schema, or with schema version `1`, must be re-exported. + +The full rules, including series fields and cell conversions, are in +[Schema and samples](/format/schema/). diff --git a/docs/src/content/docs/guides/trust.md b/docs/src/content/docs/guides/trust.md new file mode 100644 index 0000000..1418fcd --- /dev/null +++ b/docs/src/content/docs/guides/trust.md @@ -0,0 +1,66 @@ +--- +title: What is verified +description: Which parts of a bundle mlbundle checks, which it recomputes from the model, and which remain the author's unauthenticated declarations. +--- + +The author of a bundle may be an attacker. mlbundle therefore treats everything in it as a claim +and sorts each claim into one of three groups. + +## Recomputed from the model on `load` + +`load` and `mlbundle verify` rebuild the model through a closed adapter and extract these values +with the same code the writer used. Any difference in canonical JSON bytes raises +`ClaimMismatchError`. + +- library, estimator class and kind; +- hyperparameters the native format can recover; +- feature, output and per-output class counts; +- native feature names and model-exposed dtypes; +- class labels owned by the model, and labels bound to XGBoost or present in an ONNX graph; +- prediction state commitments (`state_sha256`) for scikit-learn and composites; +- the complete `structure.json`: trees, ensembles, coefficients, importances, XGBoost dumps and + CatBoost's symmetric trees; +- schema reports, through the verified class labels. + +A reviewer never sees a tree that differs from the model used for prediction. + +## Validated by `inspect` and `load` + +These checks need no model library: + +- ZIP layout, ZIP64 records, CRCs and compression; only allowed member names, no paths, symlinks, + duplicates, encryption or bytes outside the archive layout; +- member count, sizes, compression ratio and JSON depth within [limits](/reference/limits/); +- a strict manifest with exact types and a known format version; +- SHA-256 and size of every member except the manifest, which cannot contain its own hash; +- schema coverage, mappings by feature origin and report shape; sample keys. + +## Declarations nobody can verify + +| Declaration | Why it cannot be checked | +| --- | --- | +| Training environment and package versions | Recorded by the writer; any author can edit them. | +| Training dtypes | Describe a frame that is not in the bundle. | +| Schema `source`, separator and nullability | Inference history is not reproducible from the bundle. | +| Declared or frame feature names | Labels for a model without native names; count and alignment are checked. | +| `target_names`, `user_metadata`, composite step names | Free author text. | +| XGBoost label binding history | Load verifies consistency with the native copy, not how it was trained. | +| ONNX labels for a label-free graph | Only their count is checked. | +| Samples | Real rows as the author wrote them. | + +## What hashes do not prove + +SHA-256 detects corruption, not tampering: an attacker can replace a member and update its hash. +There is no signing, so nothing proves who trained a model, its training history, quality or +fairness. Bundle signing is out of scope. + +## What a runtime still owns + +Pickle-free formats do not sandbox native parsers or inference. A service that accepts uploads must +still provide authentication, authorization, process isolation and memory, CPU and row limits, and +keep model libraries patched. mlbundle's safety ceilings bound archive parsing and model state, not +process memory or the rows you predict. + +mlbundle has not had an independent security review. Do not accept bundles from untrusted parties. +Read the [security policy](/security/) for the threat model, guarantees and private vulnerability +reporting. diff --git a/docs/src/content/docs/index.md b/docs/src/content/docs/index.md new file mode 100644 index 0000000..0c7cc20 --- /dev/null +++ b/docs/src/content/docs/index.md @@ -0,0 +1,89 @@ +--- +title: mlbundle documentation +description: Package a trained model, its manifest, a locally inferred schema and model structure in one verifiable .mlbundle file, without pickle. +tableOfContents: false +hero: + title: One verifiable file for a trained model. + tagline: mlbundle puts the model in its native format, a manifest of model and training-environment metadata, a locally inferred input schema and the model's structure into one .mlbundle file. Before it hands you the model, the reader recomputes every model-derived claim and rejects disagreement. + actions: + - text: Quick start + link: /getting-started/quick-start/ + variant: primary + - text: What is verified + link: /guides/trust/ + variant: secondary + - text: Format 1 contract + link: /format/ + variant: minimal +--- + +```python title="train.py" +from sklearn.tree import DecisionTreeClassifier + +from mlbundle import export + +rows = [[0.0, 1.0], [1.0, 0.0], [0.1, 0.9], [0.9, 0.1]] +model = DecisionTreeClassifier(random_state=7).fit(rows, ["cold", "warm"] * 2) +export(model, "trained.mlbundle", feature_names=["temperature", "humidity"]) +``` + +```python title="serve.py" +from mlbundle import load + +bundle = load("trained.mlbundle") # verified before it is returned +print(bundle.predict_proba([[0.0, 1.0]])) # [[1.0, 0.0]] +print(bundle.predict_record({"0": "0.0", "1": "true"})[0].predicted_label) # cold +``` + +No pickle, joblib or cloudpickle is involved at any step. scikit-learn models are stored with +skops against a closed allowlist, XGBoost as native UBJSON, CatBoost as native CBM, and ONNX files +byte for byte. Loading rebuilds the model through mlbundle's own adapter, extracts its claims and +structure with the same code the writer used, and rejects the bundle when they disagree. + +:::note[Before you accept uploads] +Format 1 is not frozen, so a future change may require re-exporting bundles. The security design +has not had an independent review: do not accept bundles from untrusted parties. +::: + +## Find your path + +
+ + Bundle a first model + Export a classifier, inspect it, load it and predict. + + + Control what goes in + Feature names, labels, targets, metadata and overwrite. + + + Keep data local + What the schema carries, and why samples need sample_rows=N. + + + Read a bundle + inspect for metadata, load for a verified model. + + + Predict from a form + Records keyed by the schema, frozen per-output results. + + + Check your model + scikit-learn, XGBoost, CatBoost, ONNX and composite pipelines. + +
+ +## Why a bundle + +Uploading a pickled model and a DataFrame leaves three gaps that mlbundle closes: + +| Gap | What mlbundle does | +| --- | --- | +| Loading a pickle can execute code. | Closed adapters use skops, native UBJSON/CBM or the original ONNX file. There is no pickle fallback. | +| The training versions are lost. | The manifest records Python, OS, architecture and installed package versions next to model and feature metadata. | +| Training data leaves the machine. | Schema inference runs locally. Rows are included only when you ask for them explicitly. | + +mlbundle is the training-side companion to MLSuite, whose runtime accepts only `.mlbundle` +uploads. It is open source under the MIT license and part of [eshkra](https://eshkra.dev). Read +the source on [GitHub](https://github.com/UlloaSP/mlbundle). diff --git a/docs/src/content/docs/reference/api.md b/docs/src/content/docs/reference/api.md new file mode 100644 index 0000000..c46d611 --- /dev/null +++ b/docs/src/content/docs/reference/api.md @@ -0,0 +1,141 @@ +--- +title: Python API +description: Signatures and contracts of the public mlbundle symbols exported from the package root. +--- + +The package root exports the supported API. Everything else, including adapters and container +internals, is private and can change without notice. + +```python +from mlbundle import ( + FORMAT_NAME, # "mlbundle" + FORMAT_VERSION, # "1" + ArchiveLimits, + LoadedBundle, + Manifest, + MetadataView, + OutputPrediction, + export, + inspect, + load, +) +``` + +Model records are strict, frozen Pydantic models: they reject unknown fields and type coercion. + +## Functions + +### `export` + +```python +def export( + model: object, + destination: str | PathLike[str], + *, + training_data: object | None = None, + feature_names: Sequence[str] | None = None, + class_labels: Sequence[Sequence[Label]] | None = None, + target_names: Sequence[str] | None = None, + sample_rows: int | None = None, + onehot_separator: str = "__", + metadata: Mapping[str, JsonValue] | None = None, + overwrite: bool = False, + limits: ArchiveLimits | None = None, +) -> MetadataView +``` + +Publishes a bundle only after its claims survive a native serialization round trip and the reader +validates it. Requires the `schema` extra. `Label` is a JSON scalar class label. Every argument is +described in [Export a bundle](/guides/export/#arguments). + +### `inspect` + +```python +def inspect(source: BundleSource, *, limits: ArchiveLimits | None = None) -> MetadataView +``` + +Validates the container, manifest, JSON bounds, hashes, schema and samples without importing a +model library. It cannot attest model-derived claims. + +### `load` + +```python +def load(source: BundleSource, *, limits: ArchiveLimits | None = None) -> LoadedBundle +``` + +Everything `inspect` does, then deserializes through the closed adapter and verifies recomputed +claims and structure. `BundleSource` is `str | PathLike[str] | bytes | BinaryIO`. + +## Results + +### `MetadataView` + +Frozen dataclass returned by `export` and `inspect`. + +| Field | Type | +| --- | --- | +| `manifest` | `Manifest` | +| `members` | `tuple[MemberName, ...]`, in canonical order | +| `schema` | `dict[str, JsonValue] \| None` | +| `samples` | `list[dict[str, JsonValue]] \| None` | + +### `LoadedBundle` + +Frozen dataclass returned by `load`, constructed only after verification. + +| Member | Type / behaviour | +| --- | --- | +| `manifest`, `members`, `schema`, `samples` | As in `MetadataView`. | +| `structure` | `dict[str, JsonValue] \| None` | +| `native_model` | The native model object. | +| `predictor` | The adapter's uniform predictor. | +| `kind` | `ModelKind` | +| `library` | `Library` | +| `class_name` | `str`, the estimator class | +| `feature_names` | `tuple[str, ...]` | +| `feature_origin` | `FeatureOrigin` | +| `class_labels` | `list[list[Label]] \| None`, output-major | +| `predict_proba(data)` | `list`, classifiers only; otherwise `ValueError` | +| `predict(data)` | `list[float]` or rows of floats, regressors only; otherwise `ValueError` | +| `row_from_record(record)` | `list[float]` in feature order | +| `predict_record(record)` | `tuple[OutputPrediction, ...]`, one per output | + +### `OutputPrediction` + +Frozen dataclass with `output`, `kind`, `labels`, `probabilities`, `value` and `predicted_label`. +See [Records and predictions](/guides/records/#outputprediction). + +## Manifest records + +| Class | Fields | +| --- | --- | +| `Manifest` | `format_name`, `format_version`, `bundle_id`, `created_at`, `writer_version`, `model`, `features`, `target`, `environment`, `integrity`, `references`, `user_metadata` | +| `ModelInfo` | `estimator_class`, `kind`, `hyperparameters`, `n_features`, `n_classes`, `n_outputs`, `library`, `serialization`, `pipeline` and state fields | +| `FeatureInfo` | `names`, `dtypes` (training), `model_dtypes` (verified), `origin` | +| `TargetInfo` | `class_labels`, `names` | +| `TrainingEnvironment` | `python_version`, `python_implementation`, `os`, `os_version`, `architecture`, `packages`, `onnx` | +| `OnnxInfo`, `Opset` | ONNX opsets, IR version and producer | +| `IntegrityRecord` | `sha256`, `size` | +| `MemberReferences` | `schema`, `structure`, `samples` | +| `ModelClaims` | The model-derived claims the reader recomputes | + +Field types and verification rules are normative in [Format 1](/format/#manifest). + +## Enums + +All are `StrEnum`s whose values appear in the manifest. + +| Enum | Values | +| --- | --- | +| `Library` | `sklearn`, `xgboost`, `catboost`, `onnx` | +| `ModelKind` | `classifier`, `regressor` | +| `SerializationFormat` | `skops`, `ubjson`, `cbm`, `onnx`, `pipeline` | +| `FeatureOrigin` | `model`, `dataframe`, `declared`, `generated` | +| `MemberName` | `manifest.json`, `model.skops`, `model.ubj`, `model.cbm`, `model.onnx`, `schema.json`, `structure.json`, `samples.json` | + +## Limits and errors + +[`ArchiveLimits`](/reference/limits/) lowers the safety ceilings. Every exception mlbundle raises +for a bundle or model derives from `MLBundleError`; see [Errors](/reference/errors/). + +`mlbundle.__version__` is the installed package version. diff --git a/docs/src/content/docs/reference/development.md b/docs/src/content/docs/reference/development.md new file mode 100644 index 0000000..104a1cc --- /dev/null +++ b/docs/src/content/docs/reference/development.md @@ -0,0 +1,49 @@ +--- +title: Development +description: Set up a checkout of mlbundle, run its checks, and build this documentation site locally. +--- + +## The library + +mlbundle uses [uv](https://docs.astral.sh/uv/) on Python 3.14, [Ruff](https://docs.astral.sh/ruff/) +and [Pyright](https://microsoft.github.io/pyright/). The default development install includes +every extra. + +```bash +uv python install 3.14 +uv sync --locked --all-extras +uv run pre-commit install +``` + +Run the checks from the repository root: + +```bash +uv run ruff check . +uv run ruff format --check . +uv run pyright +uv run pytest +uv build +``` + +`pytest` measures branch coverage with an 80% minimum. CI runs lint and type checks and tests the +built wheel on Ubuntu and Windows, including clean installs without extras. After changing +dependency metadata, run `uv sync` and commit the updated `uv.lock`. + +Contributor rules, including the security guardrails and the 300-line file limit, are in +[AGENTS.md](https://github.com/UlloaSP/mlbundle/blob/main/AGENTS.md). Report vulnerabilities +privately as described in the [security policy](/security/#reporting-a-vulnerability). + +## This documentation + +The site is an Astro Starlight project in `docs/`, part of the repository's pnpm workspace and run +with Vite+ (`vp`). Use Node.js 22.18+, 24.11+ or 26+: + +```bash +vp install --frozen-lockfile +vp run mlbundle-docs#dev # http://127.0.0.1:4341/ +vp run mlbundle-docs#build +``` + +The [Format 1 contract](/format/) and [security policy](/security/) pages render the repository's +`docs/format*.md` and `SECURITY.md` files directly, so the specification has one source. Edit +those files, not a copy. diff --git a/docs/src/content/docs/reference/errors.md b/docs/src/content/docs/reference/errors.md new file mode 100644 index 0000000..93aafbd --- /dev/null +++ b/docs/src/content/docs/reference/errors.md @@ -0,0 +1,69 @@ +--- +title: Errors +description: The MLBundleError hierarchy, when each error is raised, and how messages stay bounded. +--- + +Validation of bundles, models, schemas and prediction inputs fails with a bounded `MLBundleError`. +Messages never echo record values or native third-party exception text. They can name a member, a +claim, an escaped and truncated feature key, an extra or a writer/version hint. Filesystem failures +and API misuse remain builtin exceptions, listed at the end. + +```python +from mlbundle import MLBundleError, load + +try: + bundle = load(b"not a bundle") +except MLBundleError as error: + print(type(error).__name__) # InvalidContainerError +``` + +## Hierarchy + +```text +MLBundleError +├── InvalidContainerError +├── LimitExceededError +├── UnsupportedFormatVersionError +├── InvalidManifestError +├── InvalidJSONError +├── IntegrityMismatchError +├── ClaimMismatchError +├── UnsupportedModelError +├── InvalidModelError +│ ├── EnvironmentMismatchError +│ └── PredictionInputError (also a ValueError) +├── InvalidSchemaError +├── InvalidSamplesError +├── MissingOptionalDependencyError +└── AdapterNotImplementedError +``` + +## When they are raised + +| Error | Raised when | Attributes | +| --- | --- | --- | +| `InvalidContainerError` | The ZIP violates the container rules: unknown, duplicate or missing members, unsafe names, bad records or CRCs, a missing schema in an old bundle. | | +| `LimitExceededError` | An input exceeds an [archive or member limit](/reference/limits/). | | +| `UnsupportedFormatVersionError` | The manifest declares a format version other than `1`. | | +| `InvalidManifestError` | The manifest fails strict validation; may hint at a different writer version. | | +| `InvalidJSONError` | A JSON member is malformed or uses an unsupported value. | | +| `IntegrityMismatchError` | A member's SHA-256 or size disagrees with the manifest. | `member` | +| `ClaimMismatchError` | A recomputed model claim or the structure differs from the bundle. | `claim` | +| `UnsupportedModelError` | Export of a model outside the adapter boundary. | `untrusted_types` | +| `InvalidModelError` | Native model content is malformed or violates the adapter contract. | | +| `EnvironmentMismatchError` | A skops load failed and the bundle declares a different scikit-learn version. | | +| `PredictionInputError` | Invalid prediction input or record. | | +| `InvalidSchemaError` | Training inputs or `schema.json` violate the schema contract. | | +| `InvalidSamplesError` | Invalid `sample_rows`, cell conversion or sample keys. | | +| `MissingOptionalDependencyError` | A needed extra is not installed. | `extra` | +| `AdapterNotImplementedError` | A library has no implemented adapter. | `library` | + +Identifiers in `member`, `claim`, `extra` and `library` are restricted to short ASCII names; +anything else is reported as `unrecognized`. + +Not every failure is an `MLBundleError`: an existing destination without `overwrite=True` raises +`FileExistsError`, and calling `predict` on a classifier (or `predict_proba` on a regressor) raises +`ValueError`. + +The [command line](/guides/cli/#errors-and-exit-codes) maps `MLBundleError` to exit code 1 and +I/O failures to exit code 2. diff --git a/docs/src/content/docs/reference/limits.md b/docs/src/content/docs/reference/limits.md new file mode 100644 index 0000000..3385f3d --- /dev/null +++ b/docs/src/content/docs/reference/limits.md @@ -0,0 +1,58 @@ +--- +title: Limits +description: The archive, member and model ceilings mlbundle enforces before allocating or loading content, and how to lower them. +--- + +Readers and writers enforce these ceilings before allocating or decompressing content. They are +safety maxima: you can lower them for a call, never raise them. + +## `ArchiveLimits` + +| Field | Default and maximum | +| --- | --- | +| `max_members` | 6 (minimum 2) | +| `max_archive_bytes` | 512 MiB | +| `max_compressed_bytes` | 512 MiB | +| `max_member_bytes` | 512 MiB | +| `max_total_bytes` | 1 GiB, all members uncompressed | +| `max_compression_ratio` | 200 | +| `max_manifest_bytes` | 8 MiB | +| `max_structure_bytes` | 256 MiB | +| `max_schema_bytes` | 16 MiB | +| `max_samples_bytes` | 16 MiB | + +A specific member cap never exceeds `max_member_bytes`. + +```python +from mlbundle import ArchiveLimits, inspect + +strict = ArchiveLimits( + max_archive_bytes=16 * 1024 * 1024, max_structure_bytes=4 * 1024 * 1024 +) +view = inspect("trained.mlbundle", limits=strict) +``` + +Values above a ceiling are rejected by validation. Pass the same object to `export`, `inspect` or +`load`; an upload service would typically lower `max_archive_bytes` to its request size. + +## Fixed ceilings + +| Item | Ceiling | +| --- | --- | +| JSON nesting depth | 32 | +| Features | 100,000 | +| Classes per output | 100,000 | +| Outputs | 1,024; XGBoost, CatBoost and ONNX: 1 | +| `user_metadata` | 1,024 top-level keys, 64 KiB, depth 8 | +| Hyperparameters | 4,096 keys, 1 MiB, depth 16 | +| XGBoost | 100,000 trees, depth 14 | +| Record string values | 64 characters | + +## What limits do not cover + +These ceilings bound archive parsing and the native state mlbundle validates. They do not cap +process memory, prediction rows or CPU time during inference. A runtime must enforce those itself. + +The normative container limits are in [Container and limits](/format/container/), native work +ceilings in [Prediction state](/format/state/#work-and-allocation-ceilings) and ONNX bounds in +[ONNX mapping](/format/onnx/). diff --git a/docs/src/fonts/jetbrains-mono-LICENSE.txt b/docs/src/fonts/jetbrains-mono-LICENSE.txt new file mode 100644 index 0000000..8f7ed67 --- /dev/null +++ b/docs/src/fonts/jetbrains-mono-LICENSE.txt @@ -0,0 +1,93 @@ +Copyright 2020 The JetBrains Mono Project Authors (https://github.com/JetBrains/JetBrainsMono) JetBrainsMono-Italic[wght].ttf: Copyright 2020 The JetBrains Mono Project Authors (https://github.com/JetBrains/JetBrainsMono) + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +http://scripts.sil.org/OFL + + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. 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This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting -- in part or in whole -- any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. 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The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE +COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL +DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM +OTHER DEALINGS IN THE FONT SOFTWARE. diff --git a/docs/src/fonts/jetbrains-mono-latin-ext-wght-normal.woff2 b/docs/src/fonts/jetbrains-mono-latin-ext-wght-normal.woff2 new file mode 100644 index 0000000000000000000000000000000000000000..01769d9e63d89d35d21547fdcfe84382a68cfe7c GIT binary patch literal 15196 zcmV-iJEO#RPew8T0RR9106Sa&6aWAK0F(#-06Oac0RR9100000000000000000000 z0000Qf^{2;XdHt+KS)+VQieSSU_Vn-K~#ZaCnf-eIxlJw2nyh2&N&N&3;;0u6ah8@ zBm;;N1Rw>8Ob3xw3d_o7BB4sPYUX$QD1!nBpdwMx6~BE~MZD zm)C#zox_XU#TJFszN)eGH1WX4Z}kjp8~Kx*yzo5FukC;BbMDlo#&L`$+bXaVD|<}- zt2TpI6;ywu1(_e6`v2#sPy`s;A->(oE2ZpX0Ot%Q7jthf zNJ9RKTZS$Wxrzow!1i=JRRH}~S?g)`X!Lv`oD6CwCOBd;)!tF-~ 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zzoI6zgqbzBl=k-8v*8Fj@EGja9x6@K%$T^_qtE@yLc j@Zp8(t&L|5@vCJ+v$xgiBf@n?#@9su;TOsOC;x0YN~JS! literal 0 HcmV?d00001 diff --git a/docs/src/navigation.ts b/docs/src/navigation.ts new file mode 100644 index 0000000..9be3166 --- /dev/null +++ b/docs/src/navigation.ts @@ -0,0 +1,48 @@ +import { specPages } from "./spec"; + +// Sidebar order follows the reader's path: start, package, consume, choose an adapter, look up. +export const sidebar = [ + { + label: "Start", + items: [ + { label: "Overview", slug: "" }, + { label: "Installation", slug: "getting-started/installation" }, + { label: "Quick start", slug: "getting-started/quick-start" }, + ], + }, + { + label: "Guides", + items: [ + { label: "Export a bundle", slug: "guides/export" }, + { label: "Inspect and load", slug: "guides/inspect-and-load" }, + { label: "Command line", slug: "guides/cli" }, + { label: "Schema and samples", slug: "guides/schema-and-samples" }, + { label: "Records and predictions", slug: "guides/records" }, + { label: "What is verified", slug: "guides/trust" }, + ], + }, + { + label: "Adapters", + items: [ + { label: "Supported models", slug: "adapters/overview" }, + { label: "scikit-learn", slug: "adapters/sklearn" }, + { label: "XGBoost", slug: "adapters/xgboost" }, + { label: "CatBoost", slug: "adapters/catboost" }, + { label: "ONNX", slug: "adapters/onnx" }, + { label: "Composite pipelines", slug: "adapters/pipelines" }, + ], + }, + { + label: "Reference", + items: [ + { label: "Python API", slug: "reference/api" }, + { label: "Errors", slug: "reference/errors" }, + { label: "Limits", slug: "reference/limits" }, + { label: "Development", slug: "reference/development" }, + ], + }, + { + label: "Format 1 contract", + items: specPages.map(({ label, route }) => ({ label, link: route })), + }, +]; diff --git a/docs/src/pages/format/[...part].astro b/docs/src/pages/format/[...part].astro new file mode 100644 index 0000000..a4c2189 --- /dev/null +++ b/docs/src/pages/format/[...part].astro @@ -0,0 +1,17 @@ +--- +import SpecPage from "../../components/SpecPage.astro"; +import { specPages } from "../../spec"; + +export function getStaticPaths() { + return specPages + .filter(({ route }) => route.startsWith("/format/")) + .map(({ file, route }) => ({ + params: { part: route.slice("/format/".length, -1) || undefined }, + props: { file }, + })); +} + +const { file } = Astro.props; +--- + + diff --git a/docs/src/pages/security.astro b/docs/src/pages/security.astro new file mode 100644 index 0000000..7373e4a --- /dev/null +++ b/docs/src/pages/security.astro @@ -0,0 +1,5 @@ +--- +import SpecPage from "../components/SpecPage.astro"; +--- + + diff --git a/docs/src/sites.ts b/docs/src/sites.ts new file mode 100644 index 0000000..61c28b6 --- /dev/null +++ b/docs/src/sites.ts @@ -0,0 +1,5 @@ +// Sibling sites: local dev servers while developing, the production hosts in built output. +const dev = import.meta.env.DEV; + +export const landingUrl = dev ? "http://localhost:4340/" : "https://mlbundle.eshkra.dev/"; +export const playgroundUrl = dev ? "http://localhost:10021/" : "https://playground.mlbundle.eshkra.dev/"; diff --git a/docs/src/spec.test.ts b/docs/src/spec.test.ts new file mode 100644 index 0000000..3d80085 --- /dev/null +++ b/docs/src/spec.test.ts @@ -0,0 +1,49 @@ +// Run with `vp run test`: the spec pages must keep resolving every relative link they contain. +import assert from "node:assert/strict"; +import { existsSync, readFileSync } from "node:fs"; +import { test } from "node:test"; +import { resolveSpecLink, specMarkdown, specPages, specTitle } from "./spec.ts"; + +const root = new URL("../../", import.meta.url); +const github = "https://github.com/UlloaSP/mlbundle/blob/main/"; + +test("relative spec links become site routes with anchors", () => { + assert.equal(resolveSpecLink("docs/format.md", "format-onnx.md"), "/format/onnx/"); + assert.equal( + resolveSpecLink("docs/format.md", "format-adapters.md#implemented-xgboost-mapping"), + "/format/adapters/#implemented-xgboost-mapping", + ); + assert.equal(resolveSpecLink("docs/format-state.md", "format.md"), "/format/"); + assert.equal(resolveSpecLink("docs/format-schema.md", "../SECURITY.md"), "/security/"); + assert.equal(resolveSpecLink("SECURITY.md", "docs/format-schema.md"), "/format/schema/"); +}); + +test("other repository files link to GitHub, absolute links are untouched", () => { + assert.equal(resolveSpecLink("SECURITY.md", "tests/test_cli.py"), `${github}tests/test_cli.py`); + assert.equal(resolveSpecLink("docs/format.md", "../AGENTS.md#guardrails"), `${github}AGENTS.md#guardrails`); + assert.equal(resolveSpecLink("docs/format.md", "#members"), "#members"); + assert.equal(resolveSpecLink("docs/format.md", "https://example.com/a.md"), "https://example.com/a.md"); +}); + +test("rendering drops only the title heading", () => { + const body = "# Title\r\n\nSee [x](format.md).\n\n## Section\n"; + assert.equal(specTitle(body, "fallback"), "Title"); + assert.equal(specMarkdown("docs/format-onnx.md", body), "\nSee [x](/format/).\n\n## Section\n"); +}); + +test("every relative link in the real spec files resolves", () => { + for (const { file } of specPages) { + const body = readFileSync(new URL(file, root), "utf8"); + assert.notEqual(specTitle(body, ""), "", `${file} has a title`); + for (const [, url] of body.matchAll(/\]\(([^)\s]+)\)/g)) { + const resolved = resolveSpecLink(file, url!); + if (resolved.startsWith(github)) { + const path = resolved.slice(github.length).split("#")[0]!; + assert.ok(existsSync(new URL(path, root)), `${file}: ${url} points to a missing file`); + } else if (!/^(https?:|#)/.test(resolved)) { + const route = resolved.split("#")[0]; + assert.ok(specPages.some((page) => page.route === route), `${file}: ${url}`); + } + } + } +}); diff --git a/docs/src/spec.ts b/docs/src/spec.ts new file mode 100644 index 0000000..dbcd850 --- /dev/null +++ b/docs/src/spec.ts @@ -0,0 +1,49 @@ +// The normative contract lives in the repository's Markdown files; the site renders them unchanged. +const repository = "https://github.com/UlloaSP/mlbundle"; + +/** Repository file (relative to the root) and its route on this site. */ +export const specPages = [ + { file: "docs/format.md", route: "/format/", label: "Format 1" }, + { file: "docs/format-container.md", route: "/format/container/", label: "Container and limits" }, + { file: "docs/format-adapters.md", route: "/format/adapters/", label: "Adapters and structure" }, + { file: "docs/format-state.md", route: "/format/state/", label: "Prediction state" }, + { file: "docs/format-schema.md", route: "/format/schema/", label: "Schema and samples" }, + { file: "docs/format-pipeline.md", route: "/format/pipeline/", label: "Composite pipelines" }, + { file: "docs/format-catboost.md", route: "/format/catboost/", label: "CatBoost mapping" }, + { file: "docs/format-onnx.md", route: "/format/onnx/", label: "ONNX mapping" }, + { file: "SECURITY.md", route: "/security/", label: "Security policy" }, +] as const; + +const byFile = new Map(specPages.map(({ file, route }) => [file, route])); + +const posix = (path: string) => path.replaceAll("\\", "/"); + +/** Resolves a relative link written for GitHub to its route here, or to the file on GitHub. */ +export function resolveSpecLink(from: string, url: string): string { + if (/^([a-z]+:|#|\/)/i.test(url)) return url; + const [path = "", hash] = url.split("#"); + const parts = posix(from).split("/").slice(0, -1); + for (const part of path.split("/")) { + if (part === "..") parts.pop(); + else if (part && part !== ".") parts.push(part); + } + const target = parts.join("/"); + const route = byFile.get(target); + const anchor = hash ? `#${hash}` : ""; + return route ? `${route}${anchor}` : `${repository}/blob/main/${target}${anchor}`; +} + +/** The spec's first-level heading, used as the page title. */ +export function specTitle(body: string, fallback: string): string { + return body.match(/^# (.+)$/m)?.[1]?.trim() ?? fallback; +} + +/** + * Prepares a spec for rendering without changing the file: drops the first-level heading, which + * Starlight renders as the title, and points repository-relative links at this site or GitHub. + */ +export function specMarkdown(file: string, body: string): string { + return body + .replace(/^# .+\r?\n/m, "") + .replace(/\]\(([^)\s]+)\)/g, (_, url: string) => `](${resolveSpecLink(file, url)})`); +} diff --git a/docs/src/styles/content.css b/docs/src/styles/content.css new file mode 100644 index 0000000..3930a9a --- /dev/null +++ b/docs/src/styles/content.css @@ -0,0 +1,134 @@ +/* Reading surface: landing typography at documentation density. */ +code, +pre, +kbd { + font-feature-settings: "calt" 0; +} + +.content-panel { + border-top-color: var(--mb-border); +} + +h1#_top { + font-weight: 700; + font-stretch: 104%; + letter-spacing: -0.035em; + line-height: 1.05; +} + +.sl-markdown-content :is(h2, h3, h4) { + font-weight: 650; + letter-spacing: -0.02em; + color: var(--mb-text); +} + +.sl-markdown-content h2 { + margin-top: 2.75rem; + padding-top: 0.25rem; +} + +.sl-markdown-content :is(p, li) { + text-wrap: pretty; +} + +.sl-markdown-content a:not(:where(.not-content *)) { + color: var(--mb-ember-ink); + text-decoration-color: color-mix(in oklab, var(--mb-ember-ink) 40%, transparent); + text-underline-offset: 0.2em; +} + +.sl-markdown-content a:not(:where(.not-content *)):hover { + text-decoration-color: currentColor; +} + +.sl-markdown-content :not(pre) > code { + padding: 0.1em 0.35em; + border: 1px solid var(--mb-border); + border-radius: 6px; + background: var(--mb-subtle); + font-size: 0.86em; + overflow-wrap: anywhere; +} + +/* Tables: hairline rows, readable cells, horizontal scroll instead of squashed columns. */ +.sl-markdown-content table { + display: block; + max-width: 100%; + overflow-x: auto; + border-collapse: collapse; + font-size: 0.9rem; +} + +.sl-markdown-content :is(th, td) { + padding: 0.6rem 0.9rem 0.6rem 0; + border-bottom: 1px solid var(--mb-border); + vertical-align: top; +} + +.sl-markdown-content th { + border-bottom-color: var(--mb-border-strong); + color: var(--mb-text); + font-weight: 600; + text-align: start; +} + +.sl-markdown-content td code { + overflow-wrap: normal; + white-space: nowrap; +} + +/* Asides keep Starlight semantics with brand surfaces. */ +.starlight-aside { + border-radius: 10px; + border-inline-start-width: 3px; +} + +.starlight-aside--note, +.starlight-aside--tip { + --sl-color-asides-text-accent: var(--mb-ember-ink); + --sl-color-asides-border: var(--mb-ember); + background: var(--mb-ember-soft); +} + +/* Steps: ember counters like the landing's numbered start section. */ +.sl-steps > li::before { + background: var(--mb-subtle); + color: var(--mb-ember-ink); + box-shadow: inset 0 0 0 1px var(--mb-border-strong); +} + +/* Starlight tabs share the landing's segmented look. */ +starlight-tabs [role="tablist"] { + border-bottom-color: var(--mb-border); +} + +starlight-tabs [role="tab"][aria-selected="true"] { + border-color: var(--mb-ember); + color: var(--mb-text); +} + +/* Cards and link cards. */ +.sl-markdown-content .sl-link-card, +.sl-markdown-content .card { + border-color: var(--mb-border); + border-radius: 12px; + background: var(--mb-bg); + box-shadow: none; +} + +.sl-markdown-content .sl-link-card:hover { + border-color: var(--mb-border-strong); + background: var(--mb-subtle); +} + +.expressive-code .frame { + box-shadow: none; +} + +.sl-markdown-content caption { + padding-bottom: 0.5rem; + color: var(--mb-text-3); + font-size: 0.8125rem; + font-weight: 600; + text-align: start; +} diff --git a/docs/src/styles/home.css b/docs/src/styles/home.css new file mode 100644 index 0000000..03ee132 --- /dev/null +++ b/docs/src/styles/home.css @@ -0,0 +1,77 @@ +/* Home hero: the landing's headline scale inside the documentation column. */ +.hero { + gap: 1.5rem; + padding-block: 1.5rem 2rem; + border-bottom: 0; +} + +.hero h1 { + font-size: clamp(2.25rem, 1.5rem + 2.8vw, 3.25rem); + font-weight: 700; + font-stretch: 104%; + letter-spacing: -0.04em; + line-height: 1.04; +} + +.hero .tagline { + max-width: 40rem; + color: var(--mb-text-2); + font-size: 1.125rem; +} + +.hero .sl-link-button { + border-radius: 999px; + font-weight: 600; +} + +.hero .sl-link-button.primary { + border-color: transparent; + background: var(--mb-text); + color: var(--mb-bg); +} + +.hero .sl-link-button.secondary { + border-color: var(--mb-border-strong); + background: transparent; + color: var(--mb-text); +} + +/* Path cards: three-column index of the documentation groups. */ +.path-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(14.5rem, 1fr)); + gap: 0.875rem; + margin: 1.5rem 0 2rem; +} + +.path-card { + display: grid; + align-content: start; + gap: 0.35rem; + padding: 1rem 1.1rem 1.1rem; + border: 1px solid var(--mb-border); + border-radius: 12px; + color: inherit; + text-decoration: none; +} + +.path-card:hover { + border-color: var(--mb-border-strong); + background: var(--mb-subtle); +} + +.path-card strong { + color: var(--mb-text); + font-size: 1rem; +} + +.path-card span { + color: var(--mb-text-2); + font-size: 0.875rem; + line-height: 1.5; +} + +.path-card code { + color: var(--mb-ember-ink); + font-size: 0.8125rem; +} diff --git a/docs/src/styles/theme.css b/docs/src/styles/theme.css new file mode 100644 index 0000000..496aa60 --- /dev/null +++ b/docs/src/styles/theme.css @@ -0,0 +1,171 @@ +/* + * The eshkra palette shared with eshkra.dev, the MLForm and MLschema docs, mapped onto Starlight. + * `--mb-*` names the brand values; Starlight's grays are derived from them so its components agree. + */ +:root, +::backdrop { + --mb-bg: #0a0a0d; + --mb-subtle: #111116; + --mb-surface: #131318; + --mb-code-bg: #0d0d11; + --mb-border: #24242c; + --mb-border-strong: #34343e; + --mb-text: #f4f4f6; + --mb-text-2: #b6b6c2; + --mb-text-3: #8e8e9b; + --mb-ember: #ff8a3d; + --mb-ember-ink: #ff9a55; + --mb-ember-soft: rgb(255 138 61 / 0.14); + + --sl-color-accent-low: #3a1a08; + --sl-color-accent: #ff8a3d; + --sl-color-accent-high: #ffc49b; + --sl-color-white: var(--mb-text); + --sl-color-gray-1: #e4e4ea; + --sl-color-gray-2: var(--mb-text-2); + --sl-color-gray-3: var(--mb-text-3); + --sl-color-gray-4: #5c5c68; + --sl-color-gray-5: var(--mb-border); + --sl-color-gray-6: var(--mb-subtle); + --sl-color-black: var(--mb-bg); + --sl-color-text: #d9d9e0; + --sl-color-text-accent: var(--mb-ember-ink); + --sl-color-bg-nav: color-mix(in oklab, var(--mb-bg) 88%, transparent); + --sl-color-bg-sidebar: var(--mb-bg); + --sl-color-bg-inline-code: var(--mb-subtle); + --sl-color-hairline: var(--mb-border); + --sl-color-hairline-light: var(--mb-border); + --sl-color-hairline-shade: var(--mb-border); + + --sl-font: var(--brand-font-sans); + --sl-font-mono: var(--brand-font-mono); + --sl-text-h1: clamp(2.125rem, 1.6rem + 1.8vw, 2.75rem); + --sl-text-h2: 1.75rem; + --sl-text-h3: 1.3125rem; + --sl-text-h4: 1.0625rem; + --sl-line-height: 1.7; + --sl-nav-height: 4rem; + --sl-content-width: 48rem; + --sl-sidebar-width: 17.5rem; +} + +:root[data-theme="light"], +[data-theme="light"] ::backdrop { + --mb-bg: #ffffff; + --mb-subtle: #f7f7f9; + --mb-surface: #ffffff; + --mb-code-bg: #fbfbfc; + --mb-border: #e7e7ee; + --mb-border-strong: #d2d2dc; + --mb-text: #0b0b10; + --mb-text-2: #474756; + --mb-text-3: #666675; + --mb-ember: #e8590c; + --mb-ember-ink: #c2410c; + --mb-ember-soft: rgb(232 89 12 / 0.09); + + --sl-color-accent-low: #fff1e8; + --sl-color-accent: #c2410c; + --sl-color-accent-high: #8a2e07; + --sl-color-gray-1: #24242e; + --sl-color-gray-4: #9a9aa8; + --sl-color-gray-7: #fbfbfc; + --sl-color-text: #26262f; +} + +::selection { + background: color-mix(in oklab, var(--mb-ember) 28%, transparent); +} + +:focus-visible { + outline: 2px solid var(--mb-ember-ink); + outline-offset: 3px; +} + +/* Header: the landing's thin sticky bar, blurred over the page. Starlight draws its one hairline. */ +header.header { + backdrop-filter: blur(14px); +} + +.parent-brand { + padding-left: 0.9rem; + border-left: 1px solid var(--mb-border-strong); + color: var(--mb-text-3); + font-size: 0.8125rem; + text-decoration: none; + white-space: nowrap; +} + +.parent-brand:hover { + color: var(--mb-ember-ink); +} + +/* Search reads as a quiet field rather than a second toolbar. */ +site-search button[data-open-modal] { + border-color: var(--mb-border); + border-radius: 999px; + background: var(--mb-subtle); +} + +site-search button[data-open-modal]:hover { + border-color: var(--mb-border-strong); +} + +/* Sidebar: subtle group headings, ember marker on the current page. */ +.sidebar-pane { + border-inline-end: 1px solid var(--mb-border); +} + +.sidebar-content .top-level > li > details > summary .group-label > span, +.sidebar-content .top-level > li > .group-label { + font-size: 0.8125rem; + font-weight: 600; + color: var(--mb-text); +} + +.sidebar-content a { + border-radius: 8px; + font-size: 0.875rem; + color: var(--mb-text-2); +} + +.sidebar-content a:hover { + color: var(--mb-text); + background: var(--mb-subtle); +} + +.sidebar-content a[aria-current="page"], +.sidebar-content a[aria-current="page"]:hover { + background: var(--mb-ember-soft); + color: var(--mb-ember-ink); + font-weight: 600; + box-shadow: inset 2px 0 0 var(--mb-ember); +} + +/* Table of contents mirrors the sidebar's current-page marker. */ +starlight-toc a { + color: var(--mb-text-3); +} + +starlight-toc a[aria-current="true"] { + color: var(--mb-ember-ink); +} + +/* Pagination cards use the landing's window surface. */ +.pagination-links a { + border-color: var(--mb-border); + border-radius: 12px; + box-shadow: none; +} + +.pagination-links a:hover { + border-color: var(--mb-border-strong); + background: var(--mb-subtle); +} + +@media (forced-colors: active) { + .sidebar-content a[aria-current="page"] { + box-shadow: none; + outline: 1px solid Highlight; + } +} diff --git a/docs/src/styles/typography.css b/docs/src/styles/typography.css new file mode 100644 index 0000000..32b9ff7 --- /dev/null +++ b/docs/src/styles/typography.css @@ -0,0 +1,54 @@ +/* Mona Sans and JetBrains Mono, self-hosted Latin subsets from Fontsource 5.3.0 (SIL OFL, licenses in ../fonts). */ + +@font-face { + font-family: "Mona Sans Variable"; + font-style: normal; + font-display: swap; + font-weight: 200 900; + font-stretch: 75% 125%; + src: url("../fonts/mona-sans-latin-standard-normal.woff2") format("woff2"); + unicode-range: + U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, + U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; +} + +@font-face { + font-family: "Mona Sans Variable"; + font-style: normal; + font-display: swap; + font-weight: 200 900; + font-stretch: 75% 125%; + src: url("../fonts/mona-sans-latin-ext-standard-normal.woff2") format("woff2"); + unicode-range: + U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, + U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, + U+A720-A7FF; +} + +@font-face { + font-family: "JetBrains Mono Variable"; + font-style: normal; + font-display: swap; + font-weight: 100 800; + src: url("../fonts/jetbrains-mono-latin-wght-normal.woff2") format("woff2"); + unicode-range: + U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, + U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; +} + +@font-face { + font-family: "JetBrains Mono Variable"; + font-style: normal; + font-display: swap; + font-weight: 100 800; + src: url("../fonts/jetbrains-mono-latin-ext-wght-normal.woff2") format("woff2"); + unicode-range: + U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, + U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, + U+A720-A7FF; +} + +:root { + --brand-font-sans: "Mona Sans Variable", "Mona Sans", system-ui, sans-serif; + --brand-font-mono: "JetBrains Mono Variable", "JetBrains Mono", ui-monospace, monospace; +} diff --git a/docs/tsconfig.json b/docs/tsconfig.json new file mode 100644 index 0000000..8bf91d3 --- /dev/null +++ b/docs/tsconfig.json @@ -0,0 +1,5 @@ +{ + "extends": "astro/tsconfigs/strict", + "include": [".astro/types.d.ts", "**/*"], + "exclude": ["dist"] +} diff --git a/docs/wrangler.jsonc b/docs/wrangler.jsonc new file mode 100644 index 0000000..c0b3cb0 --- /dev/null +++ b/docs/wrangler.jsonc @@ -0,0 +1,12 @@ +{ + "name": "mlbundle-docs", + "compatibility_date": "2026-10-19", + "workers_dev": false, + "preview_urls": false, + "assets": { + "directory": "./dist", + "html_handling": "force-trailing-slash", + "not_found_handling": "404-page", + }, + "routes": [{ "pattern": "docs.mlbundle.eshkra.dev", "custom_domain": true }], +} diff --git a/landing/.gitignore b/landing/.gitignore new file mode 100644 index 0000000..eb1c464 --- /dev/null +++ b/landing/.gitignore @@ -0,0 +1,4 @@ +node_modules/ +dist/ +.astro/ +.wrangler/ diff --git a/landing/.prettierignore b/landing/.prettierignore new file mode 100644 index 0000000..50aa970 --- /dev/null +++ b/landing/.prettierignore @@ -0,0 +1,6 @@ +node_modules/ +dist/ +.astro/ +.wrangler/ +pnpm-lock.yaml +src/data/example.json diff --git a/landing/.prettierrc.json b/landing/.prettierrc.json new file mode 100644 index 0000000..3f6e0ab --- /dev/null +++ b/landing/.prettierrc.json @@ -0,0 +1,7 @@ +{ + "plugins": ["prettier-plugin-astro"], + "printWidth": 100, + "tabWidth": 2, + "endOfLine": "lf", + "trailingComma": "all" +} diff --git a/landing/README.md b/landing/README.md new file mode 100644 index 0000000..eac1dda --- /dev/null +++ b/landing/README.md @@ -0,0 +1,46 @@ +# MLbundle landing + +The product site intended for , built with Astro. It shares the +eshkra palette, Mona Sans and JetBrains Mono fonts, and the original MLbundle mark from the +repository's `brand/mlbundle.png`. The page follows the visitor's light or dark system preference. + +## Local development + +Install once from the repository root, which owns the pnpm workspace and lockfile: + +```sh +vp install --frozen-lockfile +``` + +Then, from this directory, with Node.js 22.18+, 24.11+, or 26+: + +```sh +vp run dev +``` + +Open . In development, docs and playground links point to the sibling +dev servers on ports 4341 and 10021; builds link the intended `*.mlbundle.eshkra.dev` hosts. +`vp run build` checks Astro/TypeScript and produces the static site in `dist/`; `vp run preview` +serves that build. Use `vp run check:format` to check formatting or `vp run format` to apply it. + +Actions deploys the tested build to Cloudflare after all checks pass on `main`. +See [website deployment](../docs/deployment.md) for triggers and configuration. +Use these package scripts rather than a bare `vp build`, which runs Vite directly and cannot build +Astro. + +## The example is real output + +`examples/train.py` trains the model whose export and load the hero shows, from the `export(` +call on. `scripts/example.py` runs it in a temporary directory with the repository's `uv` +environment and stores the run in `src/data/example.json`: the printed prediction, the bundle's +members, sizes and hashes, its manifest and schema, and the CLI output of `inspect` and `verify`. +It then edits one claim in `manifest.json` (`max_depth` from `null` to `3`) and records how +`inspect` and `verify` treat that copy. Every version, size, member and terminal line on the page +comes from this file. The build does not run Python; regenerate the stored output after changing +the example or the library: + +```sh +vp run example +``` + +The bundle id, timestamp, environment and `model.skops` hash change on every run. diff --git a/landing/astro.config.ts b/landing/astro.config.ts new file mode 100644 index 0000000..fc96e66 --- /dev/null +++ b/landing/astro.config.ts @@ -0,0 +1,19 @@ +import { fileURLToPath } from "node:url"; +import { defineConfig } from "astro/config"; + +export default defineConfig({ + site: "https://mlbundle.eshkra.dev", + trailingSlash: "always", + devToolbar: { enabled: false }, + vite: { + // The dev server must read the shared brand mark outside this package. + server: { + fs: { + allow: [ + fileURLToPath(new URL(".", import.meta.url)), + fileURLToPath(new URL("../brand", import.meta.url)), + ], + }, + }, + }, +}); diff --git a/landing/examples/train.py b/landing/examples/train.py new file mode 100644 index 0000000..ac1628d --- /dev/null +++ b/landing/examples/train.py @@ -0,0 +1,24 @@ +import pandas as pd +from sklearn.tree import DecisionTreeClassifier + +from mlbundle import export, load + +training = pd.DataFrame( + [[0.0, 1.0, "cold"], [1.0, 0.0, "warm"]] * 2, + columns=["temperature", "humidity", "target"], +) +features = training.drop(columns="target") +model = DecisionTreeClassifier(random_state=7) +model.fit(features, training["target"]) + +export( + model, + "trained.mlbundle", + training_data=training, + target_names=["target"], +) + +bundle = load("trained.mlbundle") +record = {"temperature": 0.0, "humidity": 1.0} +prediction = bundle.predict_record(record)[0] +print(prediction.predicted_label, prediction.probabilities) diff --git a/landing/package.json b/landing/package.json new file mode 100644 index 0000000..2c42c60 --- /dev/null +++ b/landing/package.json @@ -0,0 +1,28 @@ +{ + "name": "mlbundle-landing", + "version": "0.0.0", + "private": true, + "type": "module", + "scripts": { + "dev": "vp exec astro dev --host 127.0.0.1 --port 4340", + "build": "vp run typecheck && vp exec astro build", + "typecheck": "vp exec astro check", + "example": "uv run --project .. python scripts/example.py", + "check:format": "vp exec prettier --check .", + "format": "vp exec prettier --write .", + "preview": "vp exec astro preview --host 127.0.0.1 --port 4340" + }, + "dependencies": { + "astro": "7.3.2", + "sharp": "0.35.5" + }, + "devDependencies": { + "@astrojs/check": "0.9.10", + "prettier": "3.9.10", + "prettier-plugin-astro": "1.1.1", + "typescript": "6.0.3" + }, + "engines": { + "node": "^22.18.0 || ^24.11.0 || >=26.0.0" + } +} diff --git a/landing/public/_headers b/landing/public/_headers new file mode 100644 index 0000000..ca37c7b --- /dev/null +++ b/landing/public/_headers @@ -0,0 +1,3 @@ +/* + X-Content-Type-Options: nosniff + Referrer-Policy: strict-origin-when-cross-origin diff --git a/landing/public/licenses/jetbrains-mono.txt b/landing/public/licenses/jetbrains-mono.txt new file mode 100644 index 0000000..8f7ed67 --- /dev/null +++ b/landing/public/licenses/jetbrains-mono.txt @@ -0,0 +1,93 @@ +Copyright 2020 The JetBrains Mono Project Authors (https://github.com/JetBrains/JetBrainsMono) JetBrainsMono-Italic[wght].ttf: Copyright 2020 The JetBrains Mono Project Authors (https://github.com/JetBrains/JetBrainsMono) + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +http://scripts.sil.org/OFL + + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting -- in part or in whole -- any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE +COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL +DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM +OTHER DEALINGS IN THE FONT SOFTWARE. diff --git a/landing/public/licenses/mona-sans.txt b/landing/public/licenses/mona-sans.txt new file mode 100644 index 0000000..fd6728b --- /dev/null +++ b/landing/public/licenses/mona-sans.txt @@ -0,0 +1,93 @@ +Copyright 2022 The Mona Sans Project Authors (https://github.com/github/mona-sans), with Reserved Font Name "Mona" MonaSans-Italic[wdth,wght].ttf: Copyright 2022 The Mona Sans Project Authors (https://github.com/github/mona-sans), with Reserved Font Name "Mona" + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +http://scripts.sil.org/OFL + + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting -- in part or in whole -- any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE +COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL +DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM +OTHER DEALINGS IN THE FONT SOFTWARE. diff --git a/landing/public/robots.txt b/landing/public/robots.txt new file mode 100644 index 0000000..80b7cf1 --- /dev/null +++ b/landing/public/robots.txt @@ -0,0 +1,4 @@ +User-agent: * +Allow: / + +Sitemap: https://mlbundle.eshkra.dev/sitemap.xml diff --git a/landing/public/sitemap.xml b/landing/public/sitemap.xml new file mode 100644 index 0000000..9278900 --- /dev/null +++ b/landing/public/sitemap.xml @@ -0,0 +1,4 @@ + + + https://mlbundle.eshkra.dev/ + diff --git a/landing/ruff.toml b/landing/ruff.toml new file mode 100644 index 0000000..fa147f1 --- /dev/null +++ b/landing/ruff.toml @@ -0,0 +1,7 @@ +# The landing example and its capture script report to the console by design. +extend = "../pyproject.toml" + +[lint] +extend-ignore = [ + "T201", # print() output is what the page shows and what the script reports. +] diff --git a/landing/scripts/example.py b/landing/scripts/example.py new file mode 100644 index 0000000..27a9531 --- /dev/null +++ b/landing/scripts/example.py @@ -0,0 +1,90 @@ +"""Run examples/train.py and store everything the landing page shows about it. + +The page never hand-writes bundle output: members, hashes, manifest, schema, CLI +text and the tampered-bundle rejection all come from this run. Usage, from landing/: +uv run --project .. python scripts/example.py +""" + +import contextlib +import io +import json +import os +import runpy +import tempfile +import zipfile +from pathlib import Path + +import mlbundle +from mlbundle import inspect +from mlbundle.cli import main as cli + +LANDING = Path(__file__).resolve().parents[1] +SOURCE = LANDING / "examples" / "train.py" +OUTPUT = LANDING / "src" / "data" / "example.json" + + +def run_cli(*argv: str) -> dict[str, object]: + out, err = io.StringIO(), io.StringIO() + with contextlib.redirect_stdout(out), contextlib.redirect_stderr(err): + code = cli(list(argv)) + return { + "exit": code, + "stdout": out.getvalue().rstrip(), + "stderr": err.getvalue().rstrip(), + } + + +def tamper(path: Path, target: Path) -> None: + """Rewrite one model claim in manifest.json, keeping every other member intact.""" + with zipfile.ZipFile(path) as source, zipfile.ZipFile(target, "w") as out: + for info in source.infolist(): + data = source.read(info.filename) + if info.filename == "manifest.json": + manifest = json.loads(data) + manifest["model"]["hyperparameters"]["max_depth"] = 3 + data = json.dumps(manifest).encode() + out.writestr(info, data) + + +def main() -> None: + previous = Path.cwd() + with tempfile.TemporaryDirectory() as work: + os.chdir(work) + try: + printed = io.StringIO() + with contextlib.redirect_stdout(printed): + runpy.run_path(str(SOURCE), run_name="__main__") + bundle = Path("trained.mlbundle") + view = inspect(bundle) + tamper(bundle, Path("edited.mlbundle")) + result = { + "version": mlbundle.__version__, + "code": SOURCE.read_text(encoding="utf-8"), + "printed": printed.getvalue().rstrip(), + "bundleBytes": bundle.stat().st_size, + "members": [ + { + "name": member.value, + "size": record.size if record else None, + "sha256": record.sha256 if record else None, + } + for member in view.members + for record in [view.manifest.integrity.get(member)] + ], + "manifest": view.manifest.model_dump(mode="json", by_alias=True), + "schema": view.schema, + "inspect": run_cli("inspect", "trained.mlbundle"), + "verify": run_cli("verify", "trained.mlbundle"), + "editedInspect": run_cli("inspect", "edited.mlbundle"), + "editedVerify": run_cli("verify", "edited.mlbundle"), + } + finally: + os.chdir(previous) + OUTPUT.write_text( + json.dumps(result, indent=2, ensure_ascii=False) + "\n", encoding="utf-8" + ) + print(f"Wrote {OUTPUT.relative_to(LANDING)}") + + +if __name__ == "__main__": + main() diff --git a/landing/src/components/BundleCard.astro b/landing/src/components/BundleCard.astro new file mode 100644 index 0000000..02142bd --- /dev/null +++ b/landing/src/components/BundleCard.astro @@ -0,0 +1,36 @@ +--- +import { example, formatBytes } from "../data/example"; + +// What each member of the example bundle holds; names and sizes come from the stored run. +const roles: Record = { + "manifest.json": "What the model claims to be", + "model.skops": "The fitted model, in skops format", + "schema.json": "Inputs and outputs, inferred locally", + "structure.json": "The decision tree, for review", +}; +const [verdict] = example.verify.stdout.split(";"); +--- + +
+
+ trained.mlbundle + {formatBytes(example.bundleBytes)} +
+
    + {example.members.map(({ name, size }) => ( +
  • + {name} + {roles[name]} + {size !== null && {formatBytes(size)}} +
    • + ))} +
+
+ {" "} + mlbundle verify trained.mlbundle +
+ {verdict} +
+
diff --git a/landing/src/components/Formats.astro b/landing/src/components/Formats.astro new file mode 100644 index 0000000..00a71e6 --- /dev/null +++ b/landing/src/components/Formats.astro @@ -0,0 +1,54 @@ +--- +import { docs } from "../data/site"; + +// The four supported libraries and their native members, as in src/mlbundle/contract.py. +const libraries = [ + { + name: "scikit-learn", + member: "model.skops", + extra: "sklearn", + note: "skops, with types allowed by MLbundle", + }, + { name: "XGBoost", member: "model.ubj", extra: "xgboost", note: "Native UBJSON" }, + { name: "CatBoost", member: "model.cbm", extra: "catboost", note: "Native CBM" }, + { name: "ONNX", member: "model.onnx", extra: "onnx", note: "Your file, byte for byte" }, +]; +--- + +
+
+
+

Four libraries, each in its own format.

+

+ A closed adapter inside MLbundle loads each format. Nothing is imported from names written + in the bundle, and there is no pickle fallback. A model MLbundle cannot load safely is + rejected when you export it, on your machine, not after you share the file. +

+ + Supported models + +
+ + + + + + + + + + + {libraries.map(({ name, member, extra, note }) => ( + + + + + + ))} + +
LibraryStored asExtra
{name} + {member} + {note} + {extra}
+
+
diff --git a/landing/src/components/Hero.astro b/landing/src/components/Hero.astro new file mode 100644 index 0000000..c76d0a2 --- /dev/null +++ b/landing/src/components/Hero.astro @@ -0,0 +1,93 @@ +--- +import { Image } from "astro:assets"; +import { Code } from "astro:components"; +import mark from "../../../brand/mlbundle.png"; +import BundleCard from "./BundleCard.astro"; +import Icon from "./Icon.astro"; +import { example, excerpt } from "../data/example"; +import { install, links } from "../data/site"; + +const themes = { light: "github-light-default", dark: "github-dark-default" } as const; +--- + +
+
+ +

+ Your trained model, in one verifiable file. +

+

+ MLbundle packs a scikit-learn, XGBoost, CatBoost or ONNX model with its manifest and input + schema. Loading recomputes what the file claims about the model from the model itself. No + pickle anywhere. +

+
+ + Read the docs + + + Open the playground + + + GitHub + +
+
+ + {install} + + +
+
+
+
+
+
+
+ train.py + excerpt +
+ +
+ {" "} + python train.py +
+ {example.printed} +
+
+ +
+
+
+ Real output of examples/train.py with MLbundle {example.version}. +
+
+
+ diff --git a/landing/src/components/Icon.astro b/landing/src/components/Icon.astro new file mode 100644 index 0000000..6d7414e --- /dev/null +++ b/landing/src/components/Icon.astro @@ -0,0 +1,45 @@ +--- +// Stroke icons follow Lucide's 24px grid (ISC licence); the GitHub mark is filled. +interface Props { + name: "arrow-up-right" | "github" | "copy" | "package"; + size?: number; +} +const { name, size = 16 } = Astro.props; +--- + + diff --git a/landing/src/components/Inside.astro b/landing/src/components/Inside.astro new file mode 100644 index 0000000..74787f3 --- /dev/null +++ b/landing/src/components/Inside.astro @@ -0,0 +1,47 @@ +--- +import { docs } from "../data/site"; + +// Three things every bundle carries; samples are the one optional addition. +const parts = [ + { + title: "The model, in its own format", + text: "Each library keeps its native serialization. The model is stored as it is, never converted and never pickled.", + }, + { + title: "A manifest of what it claims", + text: "Class, hyperparameters, feature names and order, class labels, the Python and package versions it was trained with, and a SHA-256 hash for each of the other files.", + }, + { + title: "A schema made on your machine", + text: "MLschema describes each input from the training DataFrame locally. The DataFrame itself is not written to the bundle.", + }, +]; +--- + +
+
+
+

One file, three parts.

+

+ A .mlbundle is a ZIP with a closed list of files. Anything else + inside it is rejected before the model is read. +

+
+
    + {parts.map(({ title, text }) => ( +
  • +

    {title}

    +

    {text}

    +
    • + ))} +
+

+ Models with readable internals, such as trees and linear coefficients, also carry that + structure for review, checked against the loaded model. Training rows are added only when you + ask for them with sample_rows=N. + + Format specification + +

+
+
diff --git a/landing/src/components/Schema.astro b/landing/src/components/Schema.astro new file mode 100644 index 0000000..7e9bfc4 --- /dev/null +++ b/landing/src/components/Schema.astro @@ -0,0 +1,49 @@ +--- +import { docs } from "../data/site"; + +const travels = [ + "Feature names, order and dtypes", + "Each input's kind and whether it may be missing", + "Categories and class labels", + "The fitted model, and what it learned", +]; +const stays = [ + "The training DataFrame and its rows", + "Ranges, means and counts", + "Example rows, unless you ask for them", +]; +--- + +
+
+
+

Training data stays on your machine.

+

+ Export infers the input schema where the data already is, and keeps only the description. + Want example rows in the bundle? Pass sample_rows=N: they are real + data, so they are never added by default. +

+ + Schema and samples + +
+
+
+

In the bundle

+
    + {travels.map((item) => ( +
  • {item}
    • + ))} +
+
+
+

Left behind

+
    + {stays.map((item) => ( +
  • {item}
    • + ))} +
+
+
+
+
diff --git a/landing/src/components/Start.astro b/landing/src/components/Start.astro new file mode 100644 index 0000000..794c5b8 --- /dev/null +++ b/landing/src/components/Start.astro @@ -0,0 +1,41 @@ +--- +import Icon from "./Icon.astro"; +import Wordmark from "./Wordmark.astro"; +import { docs, family, links } from "../data/site"; + +const resources = [ + { title: "Documentation", text: "Guides and the format", href: links.docs }, + { title: "Quick start", text: "Export and load a model", href: docs.quickStart }, + { title: "Playground", text: "Try it in the browser", href: links.playground }, + { title: "GitHub", text: "Source and issues", href: links.github }, + { title: "Installation", text: "Extras and checkouts", href: docs.install }, +]; +--- + +
+
+
+

Start with one model.

+

Export a model you already have, then load it the way your application will.

+
+ +
    + {family.map(({ name, stage, href }) => ( +
  1. + {stage} + + + +
    2. + ))} +
+
+
diff --git a/landing/src/components/Terminal.astro b/landing/src/components/Terminal.astro new file mode 100644 index 0000000..ea72691 --- /dev/null +++ b/landing/src/components/Terminal.astro @@ -0,0 +1,19 @@ +--- +// A stored CLI run: the command, its real output and exit code. +interface Props { + command: string; + output: string; + exit: number; +} +const { command, output, exit } = Astro.props; +const ok = exit === 0; +--- + +
+
+ {command.split(" ")[1]} + exit {exit} +
+ 
{command}
+{output}
+
diff --git a/landing/src/components/Verify.astro b/landing/src/components/Verify.astro new file mode 100644 index 0000000..44c1e3f --- /dev/null +++ b/landing/src/components/Verify.astro @@ -0,0 +1,47 @@ +--- +import Terminal from "./Terminal.astro"; +import { example } from "../data/example"; +import { docs } from "../data/site"; + +const firstLine = (text: string) => text.split("\n")[0]; +--- + +
+
+
+

Inspect reads it. Load checks it.

+

+ inspect validates the archive, manifest and hashes without + importing a model library. load also deserializes the model, + recomputes the claims derived from it, such as hyperparameters, counts, class labels and + tree structure, and rejects the file when they differ. +

+
+
+
+ + +
+
+ The example bundle with one edit: max_depth changed from{" "} + null to 3 in the manifest. +
+
+

+ Hashes catch corrupted files, not deliberate edits, and bundles are not signed. The training + environment, schema and samples are validated but not authenticated. Treat a bundle from + someone else as untrusted input. + + What MLbundle checks + +

+
+
diff --git a/landing/src/components/Wordmark.astro b/landing/src/components/Wordmark.astro new file mode 100644 index 0000000..22caa31 --- /dev/null +++ b/landing/src/components/Wordmark.astro @@ -0,0 +1,12 @@ +--- +// The eshkra lockup for every visible product name: "ML" wide and heavy, the rest light. +interface Props { + name: "mlbundle" | "mlschema" | "mlform" | "mlsuite"; +} +const { name } = Astro.props; +--- + + + ML + {name.slice(2)} + diff --git a/landing/src/data/example.json b/landing/src/data/example.json new file mode 100644 index 0000000..acc01e0 --- /dev/null +++ b/landing/src/data/example.json @@ -0,0 +1,180 @@ +{ + "version": "0.2.0", + "code": "import pandas as pd\nfrom sklearn.tree import DecisionTreeClassifier\n\nfrom mlbundle import export, load\n\ntraining = pd.DataFrame(\n [[0.0, 1.0, \"cold\"], [1.0, 0.0, \"warm\"]] * 2,\n columns=[\"temperature\", \"humidity\", \"target\"],\n)\nfeatures = training.drop(columns=\"target\")\nmodel = DecisionTreeClassifier(random_state=7)\nmodel.fit(features, training[\"target\"])\n\nexport(\n model,\n \"trained.mlbundle\",\n training_data=training,\n target_names=[\"target\"],\n)\n\nbundle = load(\"trained.mlbundle\")\nrecord = {\"temperature\": 0.0, \"humidity\": 1.0}\nprediction = bundle.predict_record(record)[0]\nprint(prediction.predicted_label, prediction.probabilities)\n", + "printed": "cold (1.0, 0.0)", + "bundleBytes": 9227, + "members": [ + { + "name": "manifest.json", + "size": null, + "sha256": null + }, + { + "name": "model.skops", + "size": 15011, + "sha256": "13257b547ae5a12c2bc5cea20afaf68d1a36d6073c8f77f1ae676979c40375d6" + }, + { + "name": "schema.json", + "size": 388, + "sha256": "e770ad53af881faf368c686de86b2b00f0a64a1444e241985643349bf7b0bba4" + }, + { + "name": "structure.json", + "size": 511, + "sha256": "8cf658be94b826ba4f81f1a78ed2c8e5107fce656d212dc888bd8621a9068baa" + } + ], + "manifest": { + "format_name": "mlbundle", + "format_version": "1", + "bundle_id": "a9e717a9-b071-4d23-bbd1-6deedc47e6b5", + "created_at": "2026-10-19T11:17:33.114088Z", + "writer_version": "0.2.0", + "model": { + "estimator_class": "DecisionTreeClassifier", + "kind": "classifier", + "hyperparameters": { + "ccp_alpha": 0.0, + "class_weight": null, + "criterion": "gini", + "max_depth": null, + "max_features": null, + "max_leaf_nodes": null, + "min_impurity_decrease": 0.0, + "min_samples_leaf": 1, + "min_samples_split": 2, + "min_weight_fraction_leaf": 0.0, + "monotonic_cst": null, + "random_state": 7, + "splitter": "best" + }, + "n_features": 2, + "n_classes": [ + 2 + ], + "n_outputs": 1, + "state_sha256": null, + "library": "sklearn", + "serialization": "skops", + "pipeline": null + }, + "features": { + "names": [ + "temperature", + "humidity" + ], + "dtypes": [ + "float64", + "float64" + ], + "model_dtypes": null, + "origin": "model" + }, + "target": { + "class_labels": [ + [ + "cold", + "warm" + ] + ], + "names": [ + "target" + ] + }, + "environment": { + "python_version": "3.14.7", + "python_implementation": "CPython", + "os": "Windows", + "os_version": "11", + "architecture": "AMD64", + "packages": { + "catboost": "1.2.10", + "mlbundle": "0.2.0", + "mlschema": "0.3.0", + "numpy": "2.5.4", + "onnx": "1.23.0", + "onnxruntime": "1.30.0", + "pandas": "3.0.6", + "scikit-learn": "1.9.1", + "scipy": "1.18.1", + "skops": "0.16.0", + "xgboost": "3.4.1" + }, + "onnx": null + }, + "integrity": { + "model.skops": { + "sha256": "13257b547ae5a12c2bc5cea20afaf68d1a36d6073c8f77f1ae676979c40375d6", + "size": 15011 + }, + "schema.json": { + "sha256": "e770ad53af881faf368c686de86b2b00f0a64a1444e241985643349bf7b0bba4", + "size": 388 + }, + "structure.json": { + "sha256": "8cf658be94b826ba4f81f1a78ed2c8e5107fce656d212dc888bd8621a9068baa", + "size": 511 + } + }, + "references": { + "schema": "schema.json", + "structure": "structure.json", + "samples": null + }, + "user_metadata": {} + }, + "schema": { + "fields": [ + { + "kind": "number", + "label": "temperature", + "mappedTo": "temperature", + "required": true, + "step": 0.1 + }, + { + "kind": "number", + "label": "humidity", + "mappedTo": "humidity", + "required": true, + "step": 0.1 + } + ], + "onehot_separator": "__", + "reports": [ + { + "kind": "classifier", + "label": "Predicted class", + "labels": [ + "cold", + "warm" + ], + "mappedTo": 0, + "showClassProbabilities": true + } + ], + "schema_version": "2", + "source": "training_data" + }, + "inspect": { + "exit": 0, + "stdout": "Metadata and integrity validated; model claims NOT verified.\nFormat: mlbundle 1\nBundle id: a9e717a9-b071-4d23-bbd1-6deedc47e6b5\nCreated: 2026-10-19T11:17:33.114088+00:00\nWriter: \"0.2.0\"\nModel: sklearn \"DecisionTreeClassifier\" (classifier)\nCounts: features=2, outputs=1, classes=[2]\nFeatures (model): [\"temperature\", \"humidity\"]\nClass labels (output-major): [[\"cold\", \"warm\"]]\nTarget names (output-major): [\"target\"]\nMembers:\n manifest.json: validated (no self-integrity record)\n model.skops: 15011 bytes sha256=13257b547ae5a12c2bc5cea20afaf68d1a36d6073c8f77f1ae676979c40375d6\n schema.json: 388 bytes sha256=e770ad53af881faf368c686de86b2b00f0a64a1444e241985643349bf7b0bba4\n structure.json: 511 bytes sha256=8cf658be94b826ba4f81f1a78ed2c8e5107fce656d212dc888bd8621a9068baa\nTraining: {\"python\": \"3.14.7\", \"implementation\": \"CPython\", \"os\": \"Windows\", \"os_version\": \"11\", \"architecture\": \"AMD64\"}\nPackages: {\"catboost\": \"1.2.10\", \"mlbundle\": \"0.2.0\", \"mlschema\": \"0.3.0\", \"numpy\": \"2.5.4\", \"onnx\": \"1.23.0\", \"onnxruntime\": \"1.30.0\", \"pandas\": \"3.0.6\", \"scikit-learn\": \"1.9.1\", \"scipy\": \"1.18.1\", \"skops\": \"0.16.0\", \"xgboost\": \"3.4.1\"}\nONNX environment: null\nPresent: schema=yes, structure=yes, samples=no\nUser metadata: {}", + "stderr": "" + }, + "verify": { + "exit": 0, + "stdout": "Valid bundle: sklearn \"DecisionTreeClassifier\"; verified format, integrity, schema/samples and native model claims/structure.", + "stderr": "" + }, + "editedInspect": { + "exit": 0, + "stdout": "Metadata and integrity validated; model claims NOT verified.\nFormat: mlbundle 1\nBundle id: a9e717a9-b071-4d23-bbd1-6deedc47e6b5\nCreated: 2026-10-19T11:17:33.114088+00:00\nWriter: \"0.2.0\"\nModel: sklearn \"DecisionTreeClassifier\" (classifier)\nCounts: features=2, outputs=1, classes=[2]\nFeatures (model): [\"temperature\", \"humidity\"]\nClass labels (output-major): [[\"cold\", \"warm\"]]\nTarget names (output-major): [\"target\"]\nMembers:\n manifest.json: validated (no self-integrity record)\n model.skops: 15011 bytes sha256=13257b547ae5a12c2bc5cea20afaf68d1a36d6073c8f77f1ae676979c40375d6\n schema.json: 388 bytes sha256=e770ad53af881faf368c686de86b2b00f0a64a1444e241985643349bf7b0bba4\n structure.json: 511 bytes sha256=8cf658be94b826ba4f81f1a78ed2c8e5107fce656d212dc888bd8621a9068baa\nTraining: {\"python\": \"3.14.7\", \"implementation\": \"CPython\", \"os\": \"Windows\", \"os_version\": \"11\", \"architecture\": \"AMD64\"}\nPackages: {\"catboost\": \"1.2.10\", \"mlbundle\": \"0.2.0\", \"mlschema\": \"0.3.0\", \"numpy\": \"2.5.4\", \"onnx\": \"1.23.0\", \"onnxruntime\": \"1.30.0\", \"pandas\": \"3.0.6\", \"scikit-learn\": \"1.9.1\", \"scipy\": \"1.18.1\", \"skops\": \"0.16.0\", \"xgboost\": \"3.4.1\"}\nONNX environment: null\nPresent: schema=yes, structure=yes, samples=no\nUser metadata: {}", + "stderr": "" + }, + "editedVerify": { + "exit": 1, + "stdout": "", + "stderr": "mlbundle: ClaimMismatchError: bundle rejected." + } +} diff --git a/landing/src/data/example.ts b/landing/src/data/example.ts new file mode 100644 index 0000000..de6fe14 --- /dev/null +++ b/landing/src/data/example.ts @@ -0,0 +1,32 @@ +// The stored run of examples/train.py; regenerate with `vp run example`. +import data from "./example.json"; + +export interface CliRun { + exit: number; + stdout: string; + stderr: string; +} + +export const example = data as typeof data & { + inspect: CliRun; + verify: CliRun; + editedInspect: CliRun; + editedVerify: CliRun; +}; + +// The hero shows the MLbundle part of the script: everything from the export call on. +const lines = example.code.trimEnd().split("\n"); +export const excerpt = lines + .slice(lines.findIndex((line) => line.startsWith("export("))) + .join("\n"); + +const units = ["B", "KB", "MB"]; +export const formatBytes = (bytes: number): string => { + let value = bytes; + let unit = 0; + while (value >= 1000 && unit < units.length - 1) { + value /= 1000; + unit += 1; + } + return `${unit === 0 ? value : value.toFixed(1)} ${units[unit]}`; +}; diff --git a/landing/src/data/site.ts b/landing/src/data/site.ts new file mode 100644 index 0000000..d150d33 --- /dev/null +++ b/landing/src/data/site.ts @@ -0,0 +1,44 @@ +const github = "https://github.com/UlloaSP/mlbundle"; +const blob = `${github}/blob/main`; + +// Builds link the intended hosts; `vp run dev` links the sibling docs and playground dev servers. +const dev = import.meta.env.DEV; + +export const links = { + home: "https://mlbundle.eshkra.dev/", + eshkra: "https://eshkra.dev/", + docs: dev ? "http://127.0.0.1:4341/" : "https://docs.mlbundle.eshkra.dev/", + playground: dev ? "http://127.0.0.1:10021/" : "https://playground.mlbundle.eshkra.dev/", + github, + issues: `${github}/issues`, + license: `${blob}/LICENSE`, + security: `${blob}/SECURITY.md`, + format: `${blob}/docs/format.md`, + formatSchema: `${blob}/docs/format-schema.md`, + pypi: "https://pypi.org/project/mlbundle/", +}; + +// Docs pages the landing points into; the base already switches between dev and build. +export const docs = { + install: `${links.docs}getting-started/installation/`, + quickStart: `${links.docs}getting-started/quick-start/`, + inspectAndLoad: `${links.docs}guides/inspect-and-load/`, + trust: `${links.docs}guides/trust/`, + schema: `${links.docs}guides/schema-and-samples/`, + adapters: `${links.docs}adapters/overview/`, + format: `${links.docs}format/`, +}; + +export const description = + "Package a trained model, a verifiable manifest and a locally inferred schema in one .mlbundle file. Native formats for scikit-learn, XGBoost, CatBoost and ONNX, never pickle."; + +// The release install from the docs; checkout installs live on the docs installation page. +export const install = 'pip install "mlbundle[sklearn,schema]"'; + +// The eshkra path from a trained model to a reviewed prediction, as on eshkra.dev. +export const family = [ + { name: "mlbundle", stage: "Package", href: "https://mlbundle.eshkra.dev/" }, + { name: "mlschema", stage: "Describe", href: "https://mlschema.eshkra.dev/" }, + { name: "mlform", stage: "Render", href: "https://mlform.eshkra.dev/" }, + { name: "mlsuite", stage: "Run & review", href: "https://github.com/UlloaSP/mlsuite" }, +] as const; diff --git a/landing/src/fonts/jetbrains-mono-latin-ext-wght-normal.woff2 b/landing/src/fonts/jetbrains-mono-latin-ext-wght-normal.woff2 new file mode 100644 index 0000000000000000000000000000000000000000..01769d9e63d89d35d21547fdcfe84382a68cfe7c GIT binary patch literal 15196 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= await getImage({ src: mark, width: 180, format: "png" }); +const canonical = new URL(Astro.url.pathname, Astro.site); +const structuredData = { + "@context": "https://schema.org", + "@type": "SoftwareSourceCode", + name: "MLbundle", + description, + url: links.home, + codeRepository: links.github, + programmingLanguage: "Python", + runtimePlatform: "Python", + license: "https://opensource.org/license/mit", + author: { "@type": "Person", name: "Pablo Ulloa Santín", url: "https://github.com/UlloaSP" }, +}; +--- + + + + + + + + + + + + + + + {noindex && } + {title} + + + + + + + + + diff --git a/playground/package.json b/playground/package.json new file mode 100644 index 0000000..4aa9ea0 --- /dev/null +++ b/playground/package.json @@ -0,0 +1,37 @@ +{ + "name": "mlbundle-playground", + "version": "0.0.0", + "private": true, + "type": "module", + "scripts": { + "dev": "vp dev --host 127.0.0.1 --port 10021 --strictPort", + "build": "vp build", + "preview": "vp preview --host 127.0.0.1 --port 10021 --strictPort", + "typecheck": "vp exec tsc -p tsconfig.json --noEmit", + "test:unit": "vp exec node --test tests/unit/*.test.ts", + "test:runtime": "vp exec node --test tests/runtime/*.test.ts", + "test:e2e": "vp run build && vp run test:e2e:built", + "test:e2e:built": "vp exec playwright test" + }, + "dependencies": { + "@codemirror/commands": "6.11.1", + "@codemirror/lang-json": "6.0.2", + "@codemirror/lang-python": "6.2.1", + "@codemirror/language": "6.13.1", + "@codemirror/state": "6.7.6", + "@codemirror/view": "6.43.14", + "@fontsource-variable/jetbrains-mono": "5.3.0", + "@fontsource-variable/mona-sans": "5.3.0", + "@lezer/highlight": "1.2.5" + }, + "devDependencies": { + "@playwright/test": "1.64.1", + "@types/node": "26.6.5", + "pyodide": "314.0.7", + "typescript": "6.0.3", + "vite": "npm:@voidzero-dev/vite-plus-core@1.3.0" + }, + "engines": { + "node": "^22.18.0 || ^24.11.0 || >=26.0.0" + } +} diff --git a/playground/playwright.config.ts b/playground/playwright.config.ts new file mode 100644 index 0000000..95e2cd4 --- /dev/null +++ b/playground/playwright.config.ts @@ -0,0 +1,22 @@ +import { defineConfig, devices } from "@playwright/test"; + +// Tests the production build on its own port, so a running dev server on 10021 is untouched. +export default defineConfig({ + testDir: "./tests/e2e", + // First boot downloads Python, scikit-learn and pandas from jsDelivr and PyPI. + timeout: 240_000, + expect: { timeout: 120_000 }, + fullyParallel: false, + workers: 1, + use: { baseURL: "http://127.0.0.1:10022", trace: "retain-on-failure" }, + webServer: { + command: "vp preview --host 127.0.0.1 --port 10022 --strictPort", + url: "http://127.0.0.1:10022", + reuseExistingServer: false, + timeout: 120_000, + }, + projects: [ + { name: "chromium", use: { ...devices["Desktop Chrome"] } }, + { name: "mobile-chromium", use: { ...devices["Pixel 7"] } }, + ], +}); diff --git a/playground/public/_headers b/playground/public/_headers new 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The playground then +# inspects it and verifies it with mlbundle.load before showing it on the right. +view = export( + model, + "classifier.mlbundle", + training_data=training, + target_names=["weather"], + metadata={"purpose": "playground example"}, +) +view.manifest.target.class_labels diff --git a/playground/src/examples/errors.py b/playground/src/examples/errors.py new file mode 100644 index 0000000..93d9fde --- /dev/null +++ b/playground/src/examples/errors.py @@ -0,0 +1,36 @@ +# mlbundle rejects unsupported models on the training machine, before writing anything. +from sklearn.ensemble import HistGradientBoostingClassifier +from sklearn.tree import DecisionTreeClassifier + +from mlbundle import MLBundleError, export + +rows = [[0.0, 1.0], [1.0, 0.0], [0.1, 0.9], [0.9, 0.1]] +labels = ["cold", "warm", "cold", "warm"] + + +class CustomTree(DecisionTreeClassifier): + """A user subclass: not on mlbundle's closed trust list.""" + + +attempts = { + "custom subclass": lambda: export( + CustomTree().fit(rows, labels), "custom.mlbundle" + ), + "HistGradientBoosting": lambda: export( + HistGradientBoostingClassifier(max_iter=3).fit(rows * 5, labels * 5), + "hist.mlbundle", + ), + "samples without a frame": lambda: export( + DecisionTreeClassifier().fit(rows, labels), "sampled.mlbundle", sample_rows=2 + ), +} +results = [] +for name, attempt in attempts.items(): + try: + attempt() + results.append({"case": name, "error": None}) + except MLBundleError as error: + results.append( + {"case": name, "error": type(error).__name__, "message": str(error)} + ) +results diff --git a/playground/src/examples/pipeline.py b/playground/src/examples/pipeline.py new file mode 100644 index 0000000..ca31cea --- /dev/null +++ b/playground/src/examples/pipeline.py @@ -0,0 +1,15 @@ +# An all-scikit-learn Pipeline. Its claims cover every step; version 1 records no +# structure for it, because the final step only sees transformed features. +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler +from sklearn.svm import SVR + +from mlbundle import export + +rows = [[0.0, 10.0], [1.0, 12.0], [2.0, 9.0], [3.0, 15.0], [4.0, 11.0]] +target = [1.2, 2.1, 2.9, 4.2, 5.1] +model = make_pipeline(StandardScaler(), SVR(C=3.0)).fit(rows, target) + +# Without a frame, names are declared and the schema comes from one default row. +view = export(model, "pipeline.mlbundle", feature_names=["dose_mg", "weight_kg"]) +{"source": view.schema["source"], "dtypes": view.manifest.features.dtypes} diff --git a/playground/src/examples/regressor.py b/playground/src/examples/regressor.py new file mode 100644 index 0000000..2307acf --- /dev/null +++ b/playground/src/examples/regressor.py @@ -0,0 +1,24 @@ +# A small random forest regressor. Its structure lists every fitted tree. +import numpy as np +import pandas as pd +from sklearn.ensemble import RandomForestRegressor + +from mlbundle import export + +rng = np.random.default_rng(3) +frame = pd.DataFrame( + { + "area_m2": rng.uniform(30, 160, 40).round(1), + "rooms": rng.integers(1, 6, 40), + "floor": rng.integers(0, 12, 40), + } +) +frame["price_k"] = ( + frame["area_m2"] * 2.4 + frame["rooms"] * 9 + rng.normal(0, 8, 40) +).round(1) + +model = RandomForestRegressor(n_estimators=3, max_depth=3, random_state=0) +model.fit(frame.drop(columns="price_k"), frame["price_k"]) +export(model, "regressor.mlbundle", training_data=frame, target_names=["price_k"]) + +model.feature_importances_.round(3).tolist() diff --git a/playground/src/examples/ruff.toml b/playground/src/examples/ruff.toml new file mode 100644 index 0000000..f543fe9 --- /dev/null +++ b/playground/src/examples/ruff.toml @@ -0,0 +1,9 @@ +# Playground examples are interactive scripts, not library code. +extend = "../../../pyproject.toml" + +[lint] +extend-ignore = [ + "B018", # The final bare expression is the result the playground displays. + "T201", # print() output is shown in the playground console. + "ERA001", # Commented code is a suggested edit for the visitor to try. +] diff --git a/playground/src/examples/samples.py b/playground/src/examples/samples.py new file mode 100644 index 0000000..5a6b01b --- /dev/null +++ b/playground/src/examples/samples.py @@ -0,0 +1,20 @@ +# plan__basic and plan__premium become one onehot-category field. +# Samples are real rows: they are included only because sample_rows is set. +import pandas as pd +from sklearn.linear_model import LogisticRegression + +from mlbundle import export + +features = pd.DataFrame( + { + "age": [34.0, 51.0, 27.0, 45.0, 62.0, 38.0], + "plan__basic": [1, 0, 1, 0, 0, 1], + "plan__premium": [0, 1, 0, 1, 1, 0], + "active": [True, False, True, True, False, True], + } +) +churned = ["no", "yes", "no", "no", "yes", "no"] +model = LogisticRegression(max_iter=500).fit(features, churned) + +view = export(model, "samples.mlbundle", training_data=features, sample_rows=3) +[field["kind"] for field in view.schema["fields"]] diff --git a/playground/src/icons.ts b/playground/src/icons.ts new file mode 100644 index 0000000..b7e079c --- /dev/null +++ b/playground/src/icons.ts @@ -0,0 +1,27 @@ +const paths = { + menu: '', + run: '', + stop: '', + reset: '', + restart: '', + copy: '', + check: '', + error: '', + loading: '', + ready: '', + download: '', + upload: '', + docs: '', + github: '', +} as const; + +export type Icon = keyof typeof paths; +export const icon = (name: Icon) => + ``; + +/** Icon-only control: the label is its accessible name and its hover/focus tooltip. */ +export const labelIcon = (element: HTMLElement, name: Icon, label: string, tooltip = label) => { + element.innerHTML = icon(name); + element.setAttribute("aria-label", label); + element.dataset.tooltip = tooltip; +}; diff --git a/playground/src/inspector/dom.ts b/playground/src/inspector/dom.ts new file mode 100644 index 0000000..326363e --- /dev/null +++ b/playground/src/inspector/dom.ts @@ -0,0 +1,87 @@ +type Child = Node | string | number | null | undefined | false; + +/** + * Builds elements from text only. Bundle contents are untrusted (uploads included), + * so nothing from a bundle is ever parsed as HTML. + */ +export const h = ( + tag: K, + attributes: Record = {}, + ...children: Child[] +): HTMLElementTagNameMap[K] => { + const element = document.createElement(tag); + for (const [name, value] of Object.entries(attributes)) element.setAttribute(name, value); + for (const child of children) { + if (child === null || child === undefined || child === false) continue; + element.append(typeof child === "object" ? child : String(child)); + } + return element; +}; + +/** Short display for any JSON value, including mlbundle's {"$float": "nan"} tags. */ +export const display = (value: unknown): string => { + if (value === null || value === undefined) return "—"; + if (typeof value === "number") return Number.isInteger(value) ? String(value) : String(+value.toPrecision(6)); + if (typeof value === "string") return value; + if (typeof value === "object" && !Array.isArray(value) && "$float" in value) { + return String((value as { $float: unknown }).$float); + } + const text = JSON.stringify(value); + return text.length > 160 ? `${text.slice(0, 157)}…` : text; +}; + +export const formatBytes = (bytes: number) => { + if (bytes < 1024) return `${bytes} B`; + const units = ["KiB", "MiB", "GiB"]; + let value = bytes / 1024; + let unit = 0; + while (value >= 1024 && unit < units.length - 1) { + value /= 1024; + unit += 1; + } + return `${value.toFixed(value < 10 ? 1 : 0)} ${units[unit]}`; +}; + +/** An accessible data table. Rows are capped; the caption reports what is hidden. */ +export const table = ( + caption: string, + headers: readonly string[], + rows: readonly (readonly unknown[])[], + limit = 200, +) => { + const shown = rows.slice(0, limit); + const note = rows.length > limit ? ` (first ${limit} of ${rows.length})` : ""; + return h( + "div", + { class: "inspector-table", tabindex: "0", role: "region", "aria-label": caption }, + h( + "table", + {}, + h("caption", {}, `${caption}${note}`), + h("thead", {}, h("tr", {}, ...headers.map((name) => h("th", { scope: "col" }, name)))), + h( + "tbody", + {}, + ...shown.map((row) => h("tr", {}, ...row.map((cell) => h("td", {}, display(cell))))), + ), + ), + ); +}; + +/** A term/description list for key facts. */ +export const facts = (label: string, entries: readonly (readonly [string, unknown])[]) => + h( + "dl", + { class: "inspector-facts", "aria-label": label }, + ...entries.flatMap(([term, value]) => [h("dt", {}, term), h("dd", {}, display(value))]), + ); + +export const section = (title: string, ...children: Child[]) => + h("section", { class: "inspector-section" }, h("h3", {}, title), ...children); + +export const note = (text: string) => h("p", { class: "inspector-note" }, text); + +export type JsonObject = { [key: string]: unknown }; +export const asObject = (value: unknown): JsonObject => + value && typeof value === "object" && !Array.isArray(value) ? (value as JsonObject) : {}; +export const asArray = (value: unknown): unknown[] => (Array.isArray(value) ? value : []); diff --git a/playground/src/inspector/overview.ts b/playground/src/inspector/overview.ts new file mode 100644 index 0000000..d2015ac --- /dev/null +++ b/playground/src/inspector/overview.ts @@ -0,0 +1,83 @@ +import type { BundleReport } from "../runtime/protocol.ts"; +import { asArray, asObject, display, facts, formatBytes, h, note, section, table } from "./dom.ts"; + +const STATUS: Record = { + verified: { + title: "Verified with mlbundle.load", + detail: + "Inspected, then loaded through the closed sklearn adapter: model claims and structure were recomputed and matched.", + }, + inspected: { + title: "Inspected only", + detail: + "mlbundle.inspect validated the archive, limits, manifest, hashes, schema and samples. The model was not deserialized, so model claims and structure are not verified.", + }, + unverified: { + title: "Rejected by mlbundle.load", + detail: "The metadata passed inspection, but loading and recomputing the model claims failed.", + }, + rejected: { + title: "Rejected by mlbundle.inspect", + detail: "mlbundle refused this file before reading its metadata.", + }, +}; + +export const statusBanner = (bundle: BundleReport) => { + const { title, detail } = STATUS[bundle.status]; + return h( + "div", + { class: "inspector-status", "data-status": bundle.status, role: "note" }, + h("strong", {}, title), + h("span", {}, detail), + bundle.error ? h("code", { class: "inspector-error" }, bundle.error) : null, + ); +}; + +export const overview = (bundle: BundleReport) => { + const manifest = asObject(bundle.manifest); + if (!bundle.manifest) { + return [facts("File", [["File", bundle.fileName], ["Size", formatBytes(bundle.sizeBytes)]])]; + } + const model = asObject(manifest.model); + const features = asObject(manifest.features); + const target = asObject(manifest.target); + const environment = asObject(manifest.environment); + const names = asArray(features.names); + const dtypes = asArray(features.dtypes); + const labels = asArray(target.class_labels); + const targetNames = asArray(target.names); + const metadata = asObject(manifest.user_metadata); + return [ + facts("Model", [ + ["Estimator", model.estimator_class], + ["Kind", model.kind], + ["Library", model.library], + ["Serialization", model.serialization], + ["Features", model.n_features], + ["Outputs", model.n_outputs], + ["File", `${bundle.fileName} · ${formatBytes(bundle.sizeBytes)}`], + ["Format", `${display(manifest.format_name)} ${display(manifest.format_version)}`], + ["Writer", manifest.writer_version], + ["Created", manifest.created_at], + ]), + section( + "Features", + note(`Origin: ${display(features.origin)}. Training dtypes are declarations; null means none were recorded.`), + table("Features in model order", ["#", "Name", "Training dtype"], names.map((name, index) => [index, name, dtypes[index] ?? null])), + ), + section( + "Targets", + labels.length + ? table("Class labels per output", ["Output", "Name", "Labels"], labels.map((output, index) => [index, targetNames[index] ?? null, asArray(output).map(display).join(", ")])) + : note(targetNames.length ? `Regression targets: ${targetNames.map(display).join(", ")}` : "Regression output; no target names were declared."), + ), + section( + "Training environment", + note(`Python ${display(environment.python_version)} on ${display(environment.os)} ${display(environment.os_version)} (${display(environment.architecture)}). Declared by the writer, not authenticated.`), + table("Package versions", ["Package", "Version"], Object.entries(asObject(environment.packages))), + ), + Object.keys(metadata).length + ? section("User metadata", table("Author metadata", ["Key", "Value"], Object.entries(metadata))) + : null, + ]; +}; diff --git a/playground/src/inspector/panel.ts b/playground/src/inspector/panel.ts new file mode 100644 index 0000000..42f2b7f --- /dev/null +++ b/playground/src/inspector/panel.ts @@ -0,0 +1,151 @@ +import { labelIcon } from "../icons.ts"; +import type { BundleReport } from "../runtime/protocol.ts"; +import { createJsonViewer } from "../workspace/viewer.ts"; +import { formatBytes, h, note } from "./dom.ts"; +import { overview, statusBanner } from "./overview.ts"; +import { membersView, samplesView, schemaView } from "./schema.ts"; +import { structureView } from "./structure.ts"; + +export const TABS = [ + ["overview", "Overview"], + ["manifest", "Manifest"], + ["schema", "Schema"], + ["structure", "Structure"], + ["members", "Members"], + ["samples", "Samples"], + ["console", "Console"], +] as const; +export type TabId = (typeof TABS)[number][0]; + +const RAW_LIMIT = 200_000; +const STATUS_LABEL: Record = { + verified: "Verified", + inspected: "Inspected only", + unverified: "Load failed", + rejected: "Rejected", +}; + +const rawJson = (title: string, value: unknown) => { + const text = JSON.stringify(value, null, 2); + const clipped = text.length > RAW_LIMIT ? `${text.slice(0, RAW_LIMIT)}\n… [${text.length - RAW_LIMIT} more characters]` : text; + return h("details", { class: "inspector-raw" }, h("summary", {}, title), h("pre", { tabindex: "0" }, clipped)); +}; + +/** + * Bundle inspector: a bundle picker, a download action and one tab per view. + * Tabs follow the ARIA tabs pattern: arrow keys, Home and End move between them. + */ +export const createInspector = (host: HTMLElement, consoleView: HTMLElement) => { + const bar = h("div", { class: "inspector-bar" }); + const picker = h("select", { "aria-label": "Bundle" }); + const title = h("span", { class: "inspector-title" }); + const chip = h("span", { class: "inspector-chip" }); + const download = h("button", { type: "button", class: "icon-button" }); + labelIcon(download, "download", "Download .mlbundle"); + bar.append(h("label", { class: "code-select inspector-picker" }, picker), title, chip, download); + const tablist = h("div", { class: "inspector-tabs", role: "tablist", "aria-label": "Bundle views" }); + const panel = h("div", { class: "inspector-panel", role: "tabpanel", tabindex: "0", id: "inspector-panel" }); + const manifestHost = h("div", { class: "output-json" }); + const manifestViewer = createJsonViewer(manifestHost, "Manifest JSON"); + host.append(bar, tablist, panel); + + let bundles: BundleReport[] = []; + let selected = 0; + let active: TabId = "overview"; + let empty = "Run the code to export a bundle, or open a .mlbundle file to inspect it."; + const tabs = TABS.map(([id, label]) => { + const tab = h("button", { type: "button", role: "tab", id: `tab-${id}`, "aria-controls": "inspector-panel" }, label); + tab.addEventListener("click", () => show(id)); + return tab; + }); + tablist.append(...tabs); + tablist.addEventListener("keydown", (event) => { + const index = TABS.findIndex(([id]) => id === active); + const next = { ArrowRight: index + 1, ArrowLeft: index - 1, Home: 0, End: TABS.length - 1 }[event.key]; + if (next === undefined) return; + event.preventDefault(); + const target = (next + TABS.length) % TABS.length; + show(TABS[target]![0]); + tabs[target]!.focus(); + }); + + const content = (bundle: BundleReport | undefined): (Node | null)[] => { + if (active === "console") return [consoleView]; + if (!bundle) return [note(empty)]; + if (active === "manifest") { + if (!bundle.manifest) return [statusBanner(bundle)]; + manifestViewer.show(JSON.stringify(bundle.manifest, null, 2)); + return [manifestHost]; + } + if (!bundle.manifest) return [statusBanner(bundle)]; + switch (active) { + case "overview": + return [statusBanner(bundle), ...overview(bundle)]; + case "schema": + return [...schemaView(bundle), bundle.schema ? rawJson("schema.json", bundle.schema) : null]; + case "structure": + return [...structureView(bundle), bundle.structure ? rawJson("structure.json", bundle.structure) : null]; + case "members": + return membersView(bundle); + default: + return samplesView(bundle); + } + }; + + const render = () => { + const bundle = bundles[selected]; + for (const [index, tab] of tabs.entries()) { + const current = TABS[index]![0] === active; + tab.setAttribute("aria-selected", String(current)); + tab.tabIndex = current ? 0 : -1; + } + panel.setAttribute("aria-labelledby", `tab-${active}`); + panel.classList.toggle("is-manifest", active === "manifest" && Boolean(bundle?.manifest)); + panel.replaceChildren(...content(bundle).filter((item): item is Node => item !== null)); + panel.scrollTop = 0; + bar.hidden = !bundle; + if (!bundle) return; + picker.parentElement!.hidden = bundles.length < 2; + title.textContent = `${bundle.fileName} · ${formatBytes(bundle.sizeBytes)}`; + chip.textContent = STATUS_LABEL[bundle.status]; + chip.dataset.status = bundle.status; + download.hidden = !bundle.bytes; + }; + const show = (id: TabId) => { + active = id; + render(); + }; + + download.addEventListener("click", () => { + const bundle = bundles[selected]; + if (!bundle?.bytes) return; + const url = URL.createObjectURL(new Blob([bundle.bytes as BlobPart], { type: "application/zip" })); + const link = h("a", { href: url, download: bundle.fileName }); + document.body.append(link); + link.click(); + link.remove(); + setTimeout(() => URL.revokeObjectURL(url), 1000); + }); + picker.addEventListener("change", () => { + selected = Number(picker.value); + render(); + }); + + render(); + return { + get active() { + return active; + }, + show, + /** Replace the shown bundles; `message` explains an empty list. */ + setBundles(next: BundleReport[], message: string) { + bundles = next; + selected = 0; + empty = message; + picker.replaceChildren(...next.map((bundle, index) => h("option", { value: String(index) }, bundle.fileName))); + render(); + }, + }; +}; + +export type Inspector = ReturnType; diff --git a/playground/src/inspector/schema.ts b/playground/src/inspector/schema.ts new file mode 100644 index 0000000..5ccd90e --- /dev/null +++ b/playground/src/inspector/schema.ts @@ -0,0 +1,48 @@ +import type { BundleReport } from "../runtime/protocol.ts"; +import { asArray, asObject, display, h, note, section, table, type JsonObject } from "./dom.ts"; + +const detail = (field: JsonObject) => { + const options = asArray(field.options).map(asObject); + if (field.kind === "onehot-category") return options.map((option) => `${display(option.label)} → ${display(option.mappedTo)}`).join(", "); + const parts: string[] = []; + for (const key of ["step", "min", "max", "options", "categories", "dtype"]) { + if (key in field) parts.push(`${key}: ${display(field[key])}`); + } + const columns = asArray(field.columns).map(asObject); + if (columns.length) parts.push(`columns: ${columns.map((column) => `${display(column.id)} (${display(column.kind)})`).join(", ")}`); + return parts.join(" · "); +}; + +export const schemaView = (bundle: BundleReport) => { + if (!bundle.schema) return [note("This bundle has no readable schema.")]; + const schema = bundle.schema; + const fields = asArray(schema.fields).map(asObject); + const reports = asArray(schema.reports).map(asObject); + return [ + note(`Schema version ${display(schema.schema_version)}, inferred locally by mlschema from ${schema.source === "default" ? "one default row of 1.0 values" : "the training frame"}. One-hot separator: "${display(schema.onehot_separator)}".`), + section( + "Fields", + table("Input fields", ["Label", "Kind", "Mapped to", "Required", "Details"], fields.map((field) => [field.label, field.kind, field.mappedTo ?? "options", field.required, detail(field)])), + ), + section( + "Reports", + table("Outputs", ["Label", "Kind", "Mapped to", "Labels"], reports.map((report) => [report.label, report.kind, report.mappedTo, asArray(report.labels).map(display).join(", ") || null])), + ), + ]; +}; + +export const membersView = (bundle: BundleReport) => [ + note("Members in archive order. The manifest records the SHA-256 and size of every other member; mlbundle checked them. Hashes detect corruption, not deliberate tampering."), + table("Archive members", ["Member", "Size (bytes)", "SHA-256"], (bundle.members ?? []).map((member) => [member.name, member.size ?? "manifest", member.sha256 ?? "—"])), +]; + +export const samplesView = (bundle: BundleReport) => { + if (!bundle.samples) { + return [note("No samples. mlbundle includes sample rows only when export is called with sample_rows=N, because they are real training data.")]; + } + const names = asArray(asObject(bundle.manifest?.features).names).map(String); + return [ + h("p", { class: "inspector-note" }, `${bundle.samples.length} explicitly requested feature rows, without target or index.`), + table("Sample rows", ["#", ...names], bundle.samples.map((row, index) => [index, ...names.map((name) => row[name])])), + ]; +}; diff --git a/playground/src/inspector/structure.ts b/playground/src/inspector/structure.ts new file mode 100644 index 0000000..f0ed106 --- /dev/null +++ b/playground/src/inspector/structure.ts @@ -0,0 +1,108 @@ +import type { BundleReport } from "../runtime/protocol.ts"; +import { asArray, asObject, display, h, note, section, type JsonObject } from "./dom.ts"; + +const NODE_LIMIT = 511; +const numbers = (value: unknown) => asArray(value).map((item) => (typeof item === "number" ? item : Number.NaN)); + +/** Relative bars for importances or coefficients, as a table so values stay readable. */ +const bars = (caption: string, names: readonly string[], values: readonly number[]) => { + const peak = Math.max(...values.map((value) => Math.abs(value)).filter(Number.isFinite), 0) || 1; + return h( + "div", + { class: "inspector-table", tabindex: "0", role: "region", "aria-label": caption }, + h( + "table", + { class: "inspector-bars" }, + h("caption", {}, caption), + h("thead", {}, h("tr", {}, h("th", { scope: "col" }, "Feature"), h("th", { scope: "col" }, "Value"), h("th", { scope: "col" }, h("span", { class: "visually-hidden" }, "Relative size")))), + h( + "tbody", + {}, + ...values.map((value, index) => { + const width = Number.isFinite(value) ? (Math.abs(value) / peak) * 100 : 0; + return h( + "tr", + {}, + h("td", {}, names[index] ?? `#${index}`), + h("td", {}, display(value)), + h("td", { "aria-hidden": "true" }, h("span", { class: "inspector-bar-fill", "data-sign": value < 0 ? "negative" : "positive", style: `width: ${width.toFixed(1)}%` })), + ); + }), + ), + ), + ); +}; + +/** One sklearn tree as an expandable outline, read straight from the native arrays. */ +export const treeOutline = (tree: JsonObject, names: readonly string[]): HTMLElement[] => { + const left = numbers(tree.children_left); + const right = numbers(tree.children_right); + const feature = numbers(tree.feature); + const threshold = asArray(tree.threshold); + const value = asArray(tree.value); + const samples = asArray(tree.n_node_samples); + let rendered = 0; + const node = (index: number, depth: number, branch: string): HTMLElement => { + rendered += 1; + const leaf = left[index] === -1 || left[index] === undefined; + const counts = `samples ${display(samples[index])} · value ${display(value[index])}`; + if (leaf || rendered >= NODE_LIMIT) { + return h("li", { class: "tree-leaf" }, h("span", { class: "tree-branch" }, branch), ` ${leaf ? "leaf" : "…"} · ${counts}`); + } + const name = names[feature[index]!] ?? `feature ${display(feature[index])}`; + const summary = h("summary", {}, h("span", { class: "tree-branch" }, branch), " ", h("strong", {}, name), ` ≤ ${display(threshold[index])}`, h("span", { class: "tree-meta" }, ` · ${counts}`)); + const children = h("ul", {}, node(left[index]!, depth + 1, "true"), node(right[index]!, depth + 1, "false")); + const open = depth < 3 ? { open: "" } : {}; + return h("li", {}, h("details", open, summary, children)); + }; + const root = h("ul", { class: "tree-outline", "aria-label": "Decision tree" }, node(0, 0, "root")); + const total = Number(tree.node_count); + return [ + note(`${display(tree.node_count)} nodes, depth ${display(tree.max_depth)}. Splits send rows with feature ≤ threshold to "true". value is sklearn's native node value, shown as stored.`), + root, + rendered < total ? note(`Showing the first ${rendered} of ${total} nodes; the raw JSON below has every node.`) : null, + ].filter((item) => item !== null); +}; + +const ensemble = (structure: JsonObject, names: readonly string[]) => { + const trees = asArray(structure.trees).map(asObject); + const host = h("div", { class: "tree-host" }); + const select = h("select", { "aria-label": "Tree" }, ...trees.map((_, index) => h("option", { value: String(index) }, `Tree ${index + 1} of ${trees.length}`))); + const show = () => host.replaceChildren(...treeOutline(trees[Number(select.value)] ?? {}, names)); + select.addEventListener("change", show); + show(); + return [note(`Estimator shape ${display(structure.estimator_shape)}.`), h("label", { class: "code-select" }, select), host]; +}; + +const linear = (structure: JsonObject, names: readonly string[]) => { + const coef = asArray(structure.coef); + const rows = Array.isArray(coef[0]) ? coef.map(numbers) : [numbers(coef)]; + return [ + note(`Intercept: ${display(structure.intercept)}. Coefficients apply to the model's input features, in order.`), + ...rows.map((row, index) => bars(rows.length > 1 ? `Coefficients, row ${index}` : "Coefficients", names, row)), + ]; +}; + +export const structureView = (bundle: BundleReport) => { + const structure = bundle.structure; + if (!structure) { + if (bundle.status === "inspected") { + return [note("Uploaded files are inspected without deserializing the model, so the playground does not show their structure: structure is only trustworthy once mlbundle.load has recomputed it from the model.")]; + } + if (bundle.status !== "verified") return [note("No verified structure: mlbundle did not load this bundle.")]; + return [note("This model has no structure in format version 1 (for example an all-scikit-learn Pipeline or an MLP). Claims still cover the whole model.")]; + } + const names = asArray(asObject(bundle.manifest?.features).names).map(String); + const family = String(structure.family); + const importances = asArray(structure.feature_importances); + const body = + family === "decision_tree" ? treeOutline(asObject(structure.tree), names) + : family === "tree_ensemble" ? ensemble(structure, names) + : family === "linear" ? linear(structure, names) + : []; + return [ + note(`Family: ${family}. Recomputed by mlbundle.load from the loaded model and compared with structure.json.`), + ...body, + importances.length ? section("Feature importances", bars("Feature importances", names, numbers(importances))) : null, + ]; +}; diff --git a/playground/src/links.ts b/playground/src/links.ts new file mode 100644 index 0000000..f1f5e92 --- /dev/null +++ b/playground/src/links.ts @@ -0,0 +1,9 @@ +// Development links point at the local landing and docs servers, as the landing does. +const dev = import.meta.env.DEV; + +export const LINKS = { + home: dev ? "http://127.0.0.1:4340/" : "https://mlbundle.eshkra.dev/", + docs: dev ? "http://127.0.0.1:4341/" : "https://docs.mlbundle.eshkra.dev/", + github: "https://github.com/UlloaSP/mlbundle", + eshkra: "https://eshkra.dev/", +} as const; diff --git a/playground/src/main.ts b/playground/src/main.ts new file mode 100644 index 0000000..e025d8d --- /dev/null +++ b/playground/src/main.ts @@ -0,0 +1,30 @@ +import "@fontsource-variable/mona-sans/standard.css"; +import "@fontsource-variable/jetbrains-mono/wght.css"; +import "./styles/index.css"; +import mark from "../../brand/mlbundle.png?url"; +import { bindMenu } from "./menu.ts"; +import { applyDesign, browserStorage, readSettings, saveSettings } from "./settings.ts"; +import { createShell } from "./shell.ts"; +import { mountViewport } from "./viewport.ts"; +import { mountWorkspace } from "./workspace/mount.ts"; + +document.querySelector('link[rel="icon"]')!.href = mark; +const storage = browserStorage(); +const settings = readSettings(storage); +applyDesign(document.documentElement, settings); +const root = document.querySelector("#root")!; +if (new URLSearchParams(location.search).has("editor")) { + // The editor frame: the page around it owns the header, the menu and the settings. + root.className = "editor-page"; + mountWorkspace(root, storage); +} else { + const shell = createShell(mark); + root.replaceWith(shell); + const updateViewport = mountViewport(shell.querySelector(".app-stage")!, settings); + bindMenu(shell, settings, (next) => { + applyDesign(document.documentElement, next); + updateViewport(next); + // Unavailable storage keeps the choice for this visit only. + saveSettings(storage, next); + }); +} diff --git a/playground/src/menu.ts b/playground/src/menu.ts new file mode 100644 index 0000000..ae5e131 --- /dev/null +++ b/playground/src/menu.ts @@ -0,0 +1,102 @@ +import { icon } from "./icons.ts"; +import { + APPEARANCES, + STYLES, + THEMES, + VIEWPORTS, + SIZE_LIMITS, + validSize, + type Settings, +} from "./settings.ts"; + +const options = (items: readonly { id: string; label: string }[]) => + items.map(({ id, label }) => ``).join(""); +const sizeInput = (key: "width" | "height", label: string) => + ``; + +/** Floating options menu, following MLForm's playground menu. Editor is the only workspace. */ +export const MENU = ` +
+ + +
`; + +export const bindMenu = ( + shell: HTMLElement, + initial: Settings, + change: (settings: Settings) => void, +) => { + let settings = initial; + const menu = shell.querySelector('[data-role="menu"]')!; + const button = menu.querySelector(".app-menu-button")!; + const panel = menu.querySelector(".app-menu-panel")!; + const editor = menu.querySelector('[data-role="editor-option"]')!; + const custom = menu.querySelector('[data-role="custom-size"]')!; + const fields = [...menu.querySelectorAll("[data-setting]")]; + + const isOpen = () => menu.classList.contains("is-open"); + const setOpen = (open: boolean, restoreFocus = false) => { + menu.classList.toggle("is-open", open); + button.setAttribute("aria-expanded", String(open)); + panel.hidden = !open; + if (open) editor.focus(); + else if (restoreFocus) button.focus(); + }; + const render = () => { + for (const field of fields) field.value = String(settings[field.dataset.setting as keyof Settings]); + custom.hidden = settings.viewport !== "custom"; + }; + const update = (field: HTMLInputElement | HTMLSelectElement) => { + const key = field.dataset.setting as keyof Settings; + const value = key === "width" || key === "height" ? Number(field.value) : field.value; + // Out-of-range sizes restore the last valid value instead of resizing. + if (typeof value === "number" && !validSize(value)) return render(); + settings = { ...settings, [key]: value }; + render(); + change(settings); + }; + + for (const field of fields) field.addEventListener("change", () => update(field)); + button.addEventListener("click", () => setOpen(!isOpen())); + button.addEventListener("keydown", (event) => { + if (event.key !== "ArrowDown") return; + event.preventDefault(); + setOpen(true); + }); + // Editor is already showing: choosing it only closes the menu. + editor.addEventListener("click", (event) => setOpen(false, event.detail === 0)); + document.addEventListener("click", (event) => { + if (isOpen() && event.target instanceof Node && !menu.contains(event.target)) setOpen(false); + }); + document.addEventListener("keydown", (event) => { + if (event.key === "Escape" && isOpen()) setOpen(false, true); + }); + menu.addEventListener("focusout", (event) => { + if (event.relatedTarget instanceof Node && !menu.contains(event.relatedTarget)) setOpen(false); + }); + // Clicks inside the editor frame never reach this document; the frame takes focus instead. + window.addEventListener("blur", () => { + if (document.activeElement instanceof HTMLIFrameElement) setOpen(false); + }); + render(); +}; diff --git a/playground/src/runtime/client.ts b/playground/src/runtime/client.ts new file mode 100644 index 0000000..f5d488a --- /dev/null +++ b/playground/src/runtime/client.ts @@ -0,0 +1,179 @@ +import type { FromWorker, OutputStream, RunReport, RuntimeVersions, ToWorker } from "./protocol.ts"; +import { BOOT_TIMEOUT_MS, RUN_TIMEOUT_MS } from "./versions.ts"; + +export interface WorkerLike { + postMessage(message: ToWorker, transfer?: Transferable[]): void; + terminate(): void; + onmessage: ((event: MessageEvent) => void) | null; + onerror: ((event: ErrorEvent) => void) | null; +} + +export type RuntimeState = + | { status: "booting"; message: string } + | { status: "ready"; versions: RuntimeVersions } + | { status: "running"; versions: RuntimeVersions } + | { status: "failed"; message: string }; + +export type RunResult = + | { kind: "finished"; report: RunReport; durationMs: number } + | { kind: "timeout"; limitMs: number } + | { kind: "cancelled" } + | { kind: "failed"; message: string } + | { kind: "unavailable" }; + +export interface RuntimeListener { + onState(state: RuntimeState): void; + onStream(stream: OutputStream, text: string): void; +} + +export interface RuntimeLimits { + bootMs: number; + runMs: number; +} + +interface PendingRun { + id: number; + timer: ReturnType; + resolve(result: RunResult): void; +} + +const describe = (error: unknown) => (error instanceof Error ? error.message : String(error)); + +/** + * Owns the Python worker. A run that times out or is cancelled terminates the + * worker, which is the only way to stop arbitrary Python, and boots a fresh one. + * A worker failure ends any pending run at once and leaves the runtime failed + * until `restart()`. + */ +export const createPythonRuntime = ( + createWorker: () => WorkerLike, + listener: RuntimeListener, + limits: RuntimeLimits = { bootMs: BOOT_TIMEOUT_MS, runMs: RUN_TIMEOUT_MS }, +) => { + let worker: WorkerLike | undefined; + let state: RuntimeState = { status: "booting", message: "Starting Python…" }; + let pending: PendingRun | undefined; + let bootTimer: ReturnType | undefined; + let nextId = 1; + + const setState = (next: RuntimeState) => { + state = next; + listener.onState(next); + }; + const settle = (result: RunResult) => { + if (!pending) return; + clearTimeout(pending.timer); + const { resolve } = pending; + pending = undefined; + if (result.kind === "finished" && state.status === "running") { + setState({ status: "ready", versions: state.versions }); + } + resolve(result); + }; + const stopWorker = () => { + clearTimeout(bootTimer); + worker?.terminate(); + worker = undefined; + }; + const fail = (reason: string) => { + const message = `${state.status === "booting" ? "Python could not start" : "Python stopped unexpectedly"}: ${reason}`; + stopWorker(); + settle({ kind: "failed", message }); + setState({ status: "failed", message }); + }; + + const handle = (message: FromWorker) => { + switch (message.type) { + case "progress": + if (state.status === "booting") setState({ status: "booting", message: message.message }); + return; + case "ready": + clearTimeout(bootTimer); + setState({ status: "ready", versions: message.versions }); + return; + case "boot-error": + fail(message.message); + return; + case "stream": + if (message.id === pending?.id) listener.onStream(message.stream, message.text); + return; + case "done": + if (message.id === pending?.id) { + settle({ kind: "finished", report: message.report, durationMs: message.durationMs }); + } + } + }; + + const start = () => { + stopWorker(); + setState({ status: "booting", message: "Starting Python…" }); + let current: WorkerLike; + try { + current = createWorker(); + } catch (error) { + fail(describe(error)); + return; + } + worker = current; + current.onmessage = ({ data }) => { + if (worker === current) handle(data); + }; + current.onerror = (event) => { + if (worker !== current) return; + event.preventDefault(); + fail(event.message || "the Python worker crashed."); + }; + bootTimer = setTimeout( + () => fail(`no response within ${limits.bootMs / 1000} seconds.`), + limits.bootMs, + ); + }; + + const submit = (message: ToWorker, transfer: Transferable[] = []): Promise => { + if (state.status !== "ready" || !worker) return Promise.resolve({ kind: "unavailable" }); + const target = worker; + const result = new Promise((resolve) => { + const timer = setTimeout(() => { + settle({ kind: "timeout", limitMs: limits.runMs }); + start(); + }, limits.runMs); + pending = { id: message.id, timer, resolve }; + }); + setState({ status: "running", versions: state.versions }); + try { + target.postMessage(message, transfer); + } catch (error) { + fail(describe(error)); + } + return result; + }; + const run = (code: string) => submit({ type: "run", id: nextId++, code }); + /** Metadata-only inspection of a visitor's file; the buffer is transferred. */ + const inspect = (name: string, data: ArrayBuffer) => + submit({ type: "inspect", id: nextId++, name, data }, [data]); + + const restart = () => { + settle({ kind: "cancelled" }); + start(); + }; + + start(); + return { + run, + inspect, + restart, + /** Stops the current run, if any, by restarting Python. */ + cancel() { + if (pending) restart(); + }, + get state() { + return state; + }, + dispose() { + settle({ kind: "cancelled" }); + stopWorker(); + }, + }; +}; + +export type PythonRuntime = ReturnType; diff --git a/playground/src/runtime/harness.py b/playground/src/runtime/harness.py new file mode 100644 index 0000000..5d6cbc8 --- /dev/null +++ b/playground/src/runtime/harness.py @@ -0,0 +1,174 @@ +"""Execution harness for the mlbundle playground. Runs inside Pyodide only.""" + +import errno +import json +import linecache +import os +import shutil +import sys +import traceback +from importlib import import_module +from pathlib import Path +from types import TracebackType + +# This module exists only in the Pyodide runtime exercised by test:runtime. +from pyodide.code import eval_code_async # pyright: ignore[reportMissingImports] + +FILENAME = "" +WORK_ROOT = Path("/playground") +MAX_BUNDLES = 8 +MIB = 1024 * 1024 +# Lowered with mlbundle's own ArchiveLimits: a browser tab is not a model server. +LIMITS_MIB = { + "max_archive_bytes": 64, + "max_compressed_bytes": 64, + "max_member_bytes": 64, + "max_total_bytes": 128, +} +_runs = 0 + + +def _no_hard_links(*_args: object, **_kwargs: object) -> None: + raise OSError(errno.ENOSYS, "Pyodide's file system has no hard links") + + +def runtime_versions(source_root: str) -> str: + """Prepare the interpreter, import the repository's mlbundle and report versions.""" + if source_root not in sys.path: + sys.path.insert(0, source_root) + # Pyodide omits os.link. mlbundle then uses its documented exclusive-create + # publication, exactly as on other file systems without hard links. + if not hasattr(os, "link"): + os.link = _no_hard_links # pyright: ignore[reportAttributeAccessIssue] + from importlib.metadata import version # noqa: PLC0415 + + import_module("mlbundle") + names = ("mlbundle", "mlschema", "scikit-learn", "skops", "pandas", "numpy") + return json.dumps( + {"python": sys.version.split()[0]} | {name: version(name) for name in names} + ) + + +def _limits(): + from mlbundle import ArchiveLimits # noqa: PLC0415 + + return ArchiveLimits(**{key: value * MIB for key, value in LIMITS_MIB.items()}) + + +def _error_text(error: BaseException) -> str: + return f"{type(error).__name__}: {error}" + + +def _metadata(view) -> dict: + manifest = json.loads(view.manifest.to_bytes()) + integrity = manifest.get("integrity", {}) + members = [ + {"name": str(name), **integrity.get(str(name), {})} for name in view.members + ] + return { + "manifest": manifest, + "members": members, + "schema": view.schema, + "samples": view.samples, + } + + +def _structure(structure: object) -> object: + if structure is None: + return None + from mlbundle.canonical import canonical_dumps # noqa: PLC0415 + + # Canonical JSON keeps non-finite numbers as {"$float": ...} tags. + return json.loads(canonical_dumps(structure, max_bytes=256 * MIB, max_depth=64)) + + +def describe_bundle(path: str, *, verify: bool) -> dict: + """Inspect metadata; when verify is set, also load and recompute model claims. + + Uploaded files are never verified here: verification deserializes the model. + """ + from mlbundle import inspect, load # noqa: PLC0415 + + target = Path(path) + report: dict = {"fileName": target.name, "sizeBytes": target.stat().st_size} + try: + report |= _metadata(inspect(target, limits=_limits())) + except Exception as error: # Every rejection is shown verbatim. + return report | {"status": "rejected", "error": _error_text(error)} + if not verify: + return report | {"status": "inspected", "structure": None} + try: + loaded = load(target, limits=_limits()) + except Exception as error: + return report | {"status": "unverified", "error": _error_text(error)} + return report | {"status": "verified", "structure": _structure(loaded.structure)} + + +def inspect_upload(path: str) -> str: + """Metadata-only inspection of a file chosen by the visitor.""" + try: + return json.dumps(describe_bundle(path, verify=False), allow_nan=False) + finally: + Path(path).unlink(missing_ok=True) + + +def _user_traceback(error: BaseException) -> TracebackType | None: + tb = error.__traceback__ + while tb is not None and tb.tb_frame.f_code.co_filename != FILENAME: + tb = tb.tb_next + return tb + + +def _format_error(error: BaseException) -> str: + lines = traceback.format_exception(type(error), error, _user_traceback(error)) + return "".join(lines).rstrip() + + +def _outcome(value: object) -> dict: + if value is None: + return {"kind": "none"} + try: + text = json.dumps(value, indent=2, ensure_ascii=False, allow_nan=False) + except (TypeError, ValueError): + return { + "kind": "repr", + "typeName": type(value).__qualname__, + "text": repr(value), + } + return {"kind": "json", "text": text} + + +def _bundles(directory: Path) -> tuple[list[dict], int]: + found = sorted(p for p in directory.glob("*.mlbundle") if p.is_file()) + reports = [describe_bundle(str(p), verify=True) for p in found[:MAX_BUNDLES]] + for report, path in zip(reports, found, strict=False): + report["path"] = str(path) + return reports, max(0, len(found) - MAX_BUNDLES) + + +async def run(code: str) -> str: + """Run code in a fresh namespace and an empty working directory. + + Every *.mlbundle the code writes into that directory is inspected and then + verified with mlbundle.load, because the visitor's own code produced it. + """ + global _runs # noqa: PLW0603 + _runs += 1 + shutil.rmtree(WORK_ROOT, ignore_errors=True) + directory = WORK_ROOT / f"run-{_runs}" + directory.mkdir(parents=True) + os.chdir(directory) + linecache.cache[FILENAME] = (len(code), None, code.splitlines(True), FILENAME) + namespace = {"__name__": "__main__"} + try: + outcome = _outcome(await eval_code_async(code, namespace, filename=FILENAME)) + except BaseException as error: + outcome = {"kind": "error", "text": _format_error(error)} + finally: + sys.stdout.flush() + sys.stderr.flush() + os.chdir("/") + bundles, skipped = _bundles(directory) + return json.dumps( + {"outcome": outcome, "bundles": bundles, "skipped": skipped}, allow_nan=False + ) diff --git a/playground/src/runtime/protocol.ts b/playground/src/runtime/protocol.ts new file mode 100644 index 0000000..2477d89 --- /dev/null +++ b/playground/src/runtime/protocol.ts @@ -0,0 +1,65 @@ +export interface RuntimeVersions { + python: string; + mlbundle: string; + mlschema: string; + "scikit-learn": string; + skops: string; + pandas: string; + numpy: string; +} + +/** What the last expression of a run produced, as reported by harness.py. */ +export type RunOutcome = + | { kind: "json"; text: string } + | { kind: "repr"; text: string; typeName: string } + | { kind: "none" } + | { kind: "error"; text: string }; + +export type Json = null | boolean | number | string | Json[] | { [key: string]: Json }; + +export interface MemberRecord { + name: string; + sha256?: string; + size?: number; +} + +/** + * One bundle as mlbundle reported it. `verified`: inspect and load both passed, so + * model claims and structure were recomputed. `inspected`: metadata only, the model + * was never deserialized. `unverified`: inspect passed, load rejected it. + * `rejected`: inspect rejected it. + */ +export interface BundleReport { + fileName: string; + sizeBytes: number; + source: "generated" | "uploaded"; + status: "verified" | "inspected" | "unverified" | "rejected"; + error?: string; + manifest?: { [key: string]: Json }; + members?: MemberRecord[]; + schema?: { [key: string]: Json } | null; + samples?: { [key: string]: Json }[] | null; + structure?: { [key: string]: Json } | null; + /** The exact bytes mlbundle wrote, for download. Generated bundles only. */ + bytes?: Uint8Array; +} + +export interface RunReport { + outcome: RunOutcome; + bundles: BundleReport[]; + /** Bundles beyond the per-run limit that were not inspected. */ + skipped: number; +} + +export type OutputStream = "stdout" | "stderr"; + +export type ToWorker = + | { type: "run"; id: number; code: string } + | { type: "inspect"; id: number; name: string; data: ArrayBuffer }; + +export type FromWorker = + | { type: "progress"; message: string } + | { type: "ready"; versions: RuntimeVersions } + | { type: "boot-error"; message: string } + | { type: "stream"; id: number; stream: OutputStream; text: string } + | { type: "done"; id: number; report: RunReport; durationMs: number }; diff --git a/playground/src/runtime/session.ts b/playground/src/runtime/session.ts new file mode 100644 index 0000000..4c9fdac --- /dev/null +++ b/playground/src/runtime/session.ts @@ -0,0 +1,100 @@ +import type { PyodideAPI } from "pyodide"; +import type { BundleReport, RunReport, RuntimeVersions } from "./protocol.ts"; +import { PYODIDE_PACKAGES, PYPI_PINS, SOURCE_ROOT } from "./versions.ts"; + +export interface PythonSession { + versions: RuntimeVersions; + run(code: string): Promise; + inspect(name: string, data: Uint8Array): Promise; +} + +/** Package-relative path, e.g. `mlbundle/inspection.py`, to file contents. */ +export type SourceTree = Readonly>; + +/** The [project] version, so importlib.metadata reports what the manifest records. */ +export const projectVersion = (pyproject: string) => { + const match = /^\[project\][^[]*?^version\s*=\s*"([^"]+)"/m.exec(pyproject); + if (!match) throw new Error("pyproject.toml declares no [project] version."); + return match[1]!; +}; + +const writeSources = (pyodide: PyodideAPI, sources: SourceTree, version: string) => { + if (!Object.hasOwn(sources, "mlbundle/__init__.py")) { + throw new Error("The mlbundle sources are missing from this build."); + } + for (const [path, text] of Object.entries(sources)) { + const target = `${SOURCE_ROOT}/${path}`; + pyodide.FS.mkdirTree(target.slice(0, target.lastIndexOf("/"))); + pyodide.FS.writeFile(target, text); + } + // Minimal distribution metadata: mlbundle reads its own version from it. + const info = `${SOURCE_ROOT}/mlbundle-${version}.dist-info`; + pyodide.FS.mkdirTree(info); + pyodide.FS.writeFile( + `${info}/METADATA`, + `Metadata-Version: 2.4\nName: mlbundle\nVersion: ${version}\n`, + ); +}; + +const UPLOAD_PATH = "/uploads/upload.mlbundle"; + +/** + * Prepare a loaded Pyodide instance: install the pinned wheels, write the real + * mlbundle sources and load the execution harness. Shared by the browser worker + * and the Node runtime tests so both exercise the same path. + */ +export const startSession = async ( + pyodide: PyodideAPI, + sources: SourceTree, + pyproject: string, + harness: string, + onProgress: (message: string) => void = () => {}, +): Promise => { + onProgress("Loading scikit-learn and pandas…"); + await pyodide.loadPackage([...PYODIDE_PACKAGES], { messageCallback: () => {} }); + onProgress("Installing pydantic, skops and mlschema…"); + const micropip = pyodide.pyimport("micropip"); + try { + await micropip.install.callKwargs([...PYPI_PINS], { deps: false }); + } finally { + micropip.destroy(); + } + onProgress("Loading mlbundle…"); + writeSources(pyodide, sources, projectVersion(pyproject)); + const namespace = pyodide.toPy({}); + pyodide.runPython(harness, { globals: namespace, filename: "harness.py" }); + const versionsOf = namespace.get("runtime_versions"); + const runCode = namespace.get("run"); + const inspectUpload = namespace.get("inspect_upload"); + const versions = JSON.parse(versionsOf(SOURCE_ROOT)) as RuntimeVersions; + versionsOf.destroy(); + + // Attach the written bytes, then remove the run directory's files. + const collect = (raw: string, source: BundleReport["source"]): RunReport => { + const report = JSON.parse(raw) as RunReport & { bundles: (BundleReport & { path?: string })[] }; + for (const bundle of report.bundles) { + bundle.source = source; + if (bundle.path && bundle.status !== "rejected") { + bundle.bytes = pyodide.FS.readFile(bundle.path) as Uint8Array; + } + delete bundle.path; + } + return report; + }; + + return { + versions, + async run(code) { + return collect(await runCode(code), "generated"); + }, + async inspect(name, data) { + pyodide.FS.mkdirTree("/uploads"); + pyodide.FS.writeFile(UPLOAD_PATH, data); + const bundle = JSON.parse(inspectUpload(UPLOAD_PATH)) as BundleReport; + // The visitor's own file name, never a path: the harness saw a fixed one. + bundle.fileName = name; + bundle.source = "uploaded"; + return { outcome: { kind: "none" }, bundles: [bundle], skipped: 0 }; + }, + }; +}; diff --git a/playground/src/runtime/sources.ts b/playground/src/runtime/sources.ts new file mode 100644 index 0000000..cbfdf05 --- /dev/null +++ b/playground/src/runtime/sources.ts @@ -0,0 +1,15 @@ +import type { SourceTree } from "./session.ts"; +import pyproject from "../../../pyproject.toml?raw"; + +// The repository's real package source, bundled at build time. Never a copy. +// state_fields.json is package data the sklearn adapter reads at import time. +const files = import.meta.glob( + ["../../../src/mlbundle/**/*.py", "../../../src/mlbundle/**/*.json"], + { query: "?raw", import: "default", eager: true }, +); + +export const MLBUNDLE_SOURCES: SourceTree = Object.fromEntries( + Object.entries(files).map(([path, text]) => [path.slice(path.indexOf("mlbundle/")), text]), +); + +export const PYPROJECT = pyproject; diff --git a/playground/src/runtime/versions.ts b/playground/src/runtime/versions.ts new file mode 100644 index 0000000..8b727c3 --- /dev/null +++ b/playground/src/runtime/versions.ts @@ -0,0 +1,40 @@ +// Pinned so every visitor gets the same interpreter and wheels. +export const PYODIDE_VERSION = "314.0.7"; +export const PYODIDE_INDEX_URL = `https://cdn.jsdelivr.net/pyodide/v${PYODIDE_VERSION}/full/`; + +// Wheels shipped with this Pyodide release: numpy 2.4.6, pandas 3.0.2, scipy 1.18.0 and +// scikit-learn 1.8.0. mlbundle's sklearn catalog is audited against 1.9.1, which has no +// build for this ABI; the manifest records the versions that actually ran. +export const PYODIDE_PACKAGES = [ + "micropip", + "numpy", + "pandas", + "scipy", + "scikit-learn", +] as const; + +// Pure-Python wheels from PyPI at the versions in the repository's uv.lock. +// Pyodide's pydantic (2.12.5) is below mlbundle's floor; pydantic-core publishes a +// pyemscripten_2026_0 build. The others have no compiled code. +export const PYPI_PINS = [ + "pydantic==2.14.1", + "pydantic-core==2.50.1", + "typing-extensions==4.16.0", + "typing-inspection==0.4.4", + "annotated-types==0.8.0", + "skops==0.16.0", + "prettytable==3.18.0", + "wcwidth==0.9.2", + "packaging==26.3", + "mlschema==0.3.0", +] as const; + +// Where the repository's src/mlbundle tree is written inside the Pyodide file system. +export const SOURCE_ROOT = "/mlbundle/src"; + +export const BOOT_TIMEOUT_MS = 240_000; +export const RUN_TIMEOUT_MS = 60_000; +export const OUTPUT_LIMIT_CHARS = 100_000; + +// Uploads above this size are refused before they are read into the browser. +export const UPLOAD_LIMIT_BYTES = 64 * 1024 * 1024; diff --git a/playground/src/runtime/worker.ts b/playground/src/runtime/worker.ts new file mode 100644 index 0000000..82b6a1e --- /dev/null +++ b/playground/src/runtime/worker.ts @@ -0,0 +1,67 @@ +import type { FromWorker, ToWorker } from "./protocol.ts"; +import harness from "./harness.py?raw"; +import { startSession, type PythonSession } from "./session.ts"; +import { MLBUNDLE_SOURCES, PYPROJECT } from "./sources.ts"; +import { OUTPUT_LIMIT_CHARS, PYODIDE_INDEX_URL } from "./versions.ts"; + +// The DOM and WebWorker libs conflict in one program; type only what is used. +const scope = self as unknown as { + postMessage(message: FromWorker, transfer?: Transferable[]): void; + addEventListener(type: "message", listener: (event: MessageEvent) => void): void; +}; +const post = (message: FromWorker, transfer: Transferable[] = []) => + scope.postMessage(message, transfer); +let runId = 0; +let written = 0; + +const forward = (stream: "stdout" | "stderr") => (line: string) => { + if (runId === 0 || written > OUTPUT_LIMIT_CHARS) return; + written += line.length + 1; + const text = + written > OUTPUT_LIMIT_CHARS ? "\n[Output truncated: over 100,000 characters.]\n" : `${line}\n`; + post({ type: "stream", id: runId, stream, text }); +}; + +const boot = async (): Promise => { + post({ type: "progress", message: "Downloading Python…" }); + // Loaded from the pinned CDN release so the loader always matches its WebAssembly. + const { loadPyodide } = (await import( + /* @vite-ignore */ `${PYODIDE_INDEX_URL}pyodide.mjs` + )) as typeof import("pyodide"); + const pyodide = await loadPyodide({ + indexURL: PYODIDE_INDEX_URL, + stdout: forward("stdout"), + stderr: forward("stderr"), + }); + return startSession(pyodide, MLBUNDLE_SOURCES, PYPROJECT, harness, (message) => + post({ type: "progress", message }), + ); +}; + +const session = boot(); +session.then( + ({ versions }) => post({ type: "ready", versions }), + (error: unknown) => + post({ type: "boot-error", message: error instanceof Error ? error.message : String(error) }), +); + +scope.addEventListener("message", async ({ data }) => { + const python = await session; + runId = data.id; + written = 0; + const started = performance.now(); + try { + const report = + data.type === "run" + ? await python.run(data.code) + : await python.inspect(data.name, new Uint8Array(data.data)); + const transfer = report.bundles.flatMap((bundle) => (bundle.bytes ? [bundle.bytes.buffer] : [])); + post({ type: "done", id: data.id, report, durationMs: performance.now() - started }, transfer); + } catch (error) { + const text = error instanceof Error ? error.message : String(error); + const report = { outcome: { kind: "error", text } as const, bundles: [], skipped: 0 }; + post({ type: "done", id: data.id, report, durationMs: 0 }); + } finally { + runId = 0; + } +}); diff --git a/playground/src/settings.ts b/playground/src/settings.ts new file mode 100644 index 0000000..e2141a3 --- /dev/null +++ b/playground/src/settings.ts @@ -0,0 +1,119 @@ +/** Options-menu settings. The catalogs mirror MLForm's playground (themes, recipes, viewports). */ +export const THEMES = [ + { id: "eshkra", label: "eshkra" }, + { id: "m3diseen", label: "M3DISEEN" }, + { id: "dosetailor", label: "DoseTailor" }, + { id: "clickhouse", label: "ClickHouse" }, + { id: "airbnb", label: "Airbnb" }, + { id: "neutral", label: "Neutral" }, + { id: "cobalt", label: "Cobalt" }, + { id: "sage", label: "Sage" }, + { id: "sunset", label: "Sunset" }, + { id: "graphite", label: "Graphite" }, +] as const; +export const STYLES = [ + { id: "default", label: "Default" }, + { id: "minimal", label: "Minimal" }, + { id: "soft", label: "Soft" }, + { id: "contrast", label: "Contrast" }, +] as const; +export const APPEARANCES = [ + { id: "auto", label: "System" }, + { id: "light", label: "Light" }, + { id: "dark", label: "Dark" }, +] as const; +export const VIEWPORTS = [ + { id: "actual", label: "Current viewport" }, + { id: "phone", label: "Phone · 390 × 844", width: 390, height: 844 }, + { id: "tablet", label: "Tablet · 820 × 1180", width: 820, height: 1180 }, + { id: "laptop", label: "Laptop · 1366 × 768", width: 1366, height: 768 }, + { id: "pc", label: "PC · 1920 × 1080", width: 1920, height: 1080 }, + { id: "ultrawide", label: "Ultrawide · 2560 × 1080", width: 2560, height: 1080 }, + { id: "custom", label: "Custom" }, +] as const; +export const SIZE_LIMITS = { min: 1, max: 8192 } as const; + +type Id = T[number]["id"]; +export interface Settings { + theme: Id; + style: Id; + appearance: Id; + viewport: Id; + width: number; + height: number; +} +export const SETTINGS_KEY = "mlbundle-playground-settings"; +export const DEFAULT_SETTINGS: Settings = { + theme: "eshkra", + style: "default", + appearance: "auto", + viewport: "actual", + width: 1280, + height: 800, +}; + +export const validSize = (value: unknown): value is number => + typeof value === "number" && + Number.isInteger(value) && + value >= SIZE_LIMITS.min && + value <= SIZE_LIMITS.max; + +const pick = (items: T, value: unknown, fallback: Id) => + (items.find(({ id }) => id === value)?.id as Id | undefined) ?? fallback; + +/** Each saved field is validated on its own, so one bad value never resets the others. */ +export const parseSettings = (value: unknown): Settings => { + const saved = value && typeof value === "object" ? (value as Record) : {}; + return { + theme: pick(THEMES, saved.theme, DEFAULT_SETTINGS.theme), + style: pick(STYLES, saved.style, DEFAULT_SETTINGS.style), + appearance: pick(APPEARANCES, saved.appearance, DEFAULT_SETTINGS.appearance), + viewport: pick(VIEWPORTS, saved.viewport, DEFAULT_SETTINGS.viewport), + width: validSize(saved.width) ? saved.width : DEFAULT_SETTINGS.width, + height: validSize(saved.height) ? saved.height : DEFAULT_SETTINGS.height, + }; +}; + +export const readSettings = (storage?: Pick): Settings => { + try { + return parseSettings(JSON.parse(storage?.getItem(SETTINGS_KEY) ?? "null")); + } catch { + return { ...DEFAULT_SETTINGS }; + } +}; + +/** Returns false when the choice cannot be stored; it still applies to this visit. */ +export const saveSettings = (storage: Pick | undefined, settings: Settings) => { + try { + storage?.setItem(SETTINGS_KEY, JSON.stringify(settings)); + return storage !== undefined; + } catch { + return false; + } +}; + +/** The simulated viewport, or null when the workspace uses the real window. */ +export const viewportSize = (settings: Settings) => { + if (settings.viewport === "actual") return null; + const preset = VIEWPORTS.find(({ id }) => id === settings.viewport)!; + const size = + "width" in preset + ? { width: preset.width, height: preset.height } + : { width: settings.width, height: settings.height }; + return { ...size, name: preset.label.split(" · ")[0]! }; +}; + +export const applyDesign = (root: HTMLElement, settings: Settings) => { + root.dataset.theme = settings.theme; + root.dataset.style = settings.style; + root.dataset.appearance = settings.appearance; +}; + +/** The browser's storage, or undefined where access is blocked (some privacy modes). */ +export const browserStorage = () => { + try { + return window.localStorage; + } catch { + return undefined; + } +}; diff --git a/playground/src/shell.ts b/playground/src/shell.ts new file mode 100644 index 0000000..88409ce --- /dev/null +++ b/playground/src/shell.ts @@ -0,0 +1,27 @@ +import { icon } from "./icons.ts"; +import { LINKS } from "./links.ts"; +import { MENU } from "./menu.ts"; +import eshkraMark from "./assets/eshkra-mark.png?url"; + +export const createShell = (mark: string) => { + const shell = document.createElement("main"); + shell.className = "app-shell"; + shell.innerHTML = ` +
+ + + MLbundle + + +
+ ${MENU} +
+ `; + return shell; +}; diff --git a/playground/src/styles/base.css b/playground/src/styles/base.css new file mode 100644 index 0000000..8562a79 --- /dev/null +++ b/playground/src/styles/base.css @@ -0,0 +1,85 @@ +:root { + font-family: var(--font-sans); + color: var(--text); + background: var(--bg); + scrollbar-color: var(--text-muted) var(--bg); + line-height: 1.5; + font-weight: 400; + font-synthesis: none; + text-rendering: optimizeLegibility; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +* { + box-sizing: border-box; +} + +html, +body { + width: 100%; + height: 100%; + margin: 0; +} + +body { + background: var(--bg); +} + +button, +select, +input { + font: inherit; + touch-action: manipulation; +} + +a { + color: inherit; + text-decoration-thickness: 1px; + text-underline-offset: 0.25em; +} + +::selection { + background: var(--accent); + color: var(--text-inverse); +} + +:focus-visible { + outline: 2px solid var(--text); + outline-offset: 2px; +} + +.visually-hidden { + position: absolute; + width: 1px; + height: 1px; + overflow: hidden; + clip-path: inset(50%); + white-space: nowrap; +} + +.app-shell { + display: flex; + flex-direction: column; + height: 100dvh; + padding-top: var(--header-height); + overflow: hidden; + accent-color: var(--accent); + caret-color: var(--text); +} + +.editor-page { + display: flex; + height: 100dvh; + overflow: hidden; +} +.editor-page > * { + flex: 1; + min-width: 0; +} + +@media (prefers-reduced-motion: reduce) { + * { + transition-duration: 0s !important; + } +} diff --git a/playground/src/styles/controls.css b/playground/src/styles/controls.css new file mode 100644 index 0000000..e5c48bd --- /dev/null +++ b/playground/src/styles/controls.css @@ -0,0 +1,54 @@ +.icon-button { + display: inline-grid; + place-items: center; + flex-shrink: 0; + width: var(--control-size); + height: var(--control-size); + padding: 0; + border: var(--border-width) solid var(--border-strong); + border-radius: var(--radius-sm); + background: var(--surface); + color: var(--text); + cursor: pointer; + transition: background var(--motion-fast); +} +.code-workspace .icon-button { + min-height: 0; + padding: 0; +} +.icon-button:hover:not(:disabled) { + background: var(--hover-surface); +} + +/* Icon-only controls name themselves in a tooltip on hover and on keyboard focus. */ +:where([data-tooltip]) { + position: relative; +} +[data-tooltip]::after { + content: attr(data-tooltip); + position: absolute; + z-index: 60; + top: calc(100% + 0.45rem); + right: 0; + width: max-content; + max-width: min(20rem, 80vw); + padding: 0.35rem 0.55rem; + border: var(--border-width) solid var(--border-strong); + border-radius: var(--radius-sm); + background: var(--surface-elevated); + color: var(--text); + font: 500 0.75rem/1.4 var(--font-sans); + text-align: left; + white-space: nowrap; + pointer-events: none; + opacity: 0; + transition: opacity var(--motion-fast); +} +[data-tooltip]:focus-visible::after { + opacity: 1; +} +@media (hover: hover) { + [data-tooltip]:hover::after { + opacity: 1; + } +} diff --git a/playground/src/styles/editor.css b/playground/src/styles/editor.css new file mode 100644 index 0000000..143c425 --- /dev/null +++ b/playground/src/styles/editor.css @@ -0,0 +1,37 @@ +:is(.code-editor, .output-json) .cm-editor { + height: 100%; + background: var(--surface); + color: var(--text); +} +:is(.code-editor, .output-json) .cm-scroller { + overflow: auto; + font: var(--code-font-size)/var(--code-line-height) var(--font-mono); + font-variant-ligatures: none; +} +:is(.code-editor, .output-json) .cm-content { + padding: 0.75rem 0; + caret-color: var(--text); +} +:is(.code-editor, .output-json) .cm-gutters { + border-color: var(--border); + background: var(--surface-muted); + color: var(--text-muted); +} +.code-editor :is(.cm-activeLine, .cm-activeLineGutter) { + background: var(--hover-surface); +} +:is(.code-editor, .output-json) .cm-editor.cm-focused { + outline: 2px solid var(--text); + outline-offset: -2px; +} +.code-editor .cm-cursor { + border-left-color: var(--text); +} +:is(.code-editor, .output-json) .cm-editor .cm-selectionBackground, +:is(.code-editor, .output-json) .cm-content ::selection { + background: color-mix(in srgb, var(--accent) 30%, transparent) !important; +} +.code-editor .cm-matchingBracket { + background: color-mix(in srgb, var(--accent) 18%, transparent); + outline: 1px solid var(--accent); +} diff --git a/playground/src/styles/header.css b/playground/src/styles/header.css new file mode 100644 index 0000000..121bde9 --- /dev/null +++ b/playground/src/styles/header.css @@ -0,0 +1,80 @@ +/* Brand header, as in MLForm's playground (brand-header.css); the menu floats at its left. */ +.app-brand-header { + position: fixed; + inset: 0 0 auto; + z-index: 40; + height: var(--header-height); + padding: 1rem 1.25rem calc(var(--header-height) - 4rem) 5rem; + background: var(--bg); + display: flex; + align-items: center; + gap: 1rem; +} +.mlbundle-brand { + display: inline-flex; + align-items: center; + gap: 0.6rem; + min-width: 0; + color: inherit; + font-size: 1.625rem; + text-decoration: none; +} +.mlbundle-brand img { + width: 1.625rem; + height: 2.5rem; + object-fit: contain; +} +.mlbundle-wordmark { + white-space: nowrap; +} +.mlbundle-wordmark .ml { + font-weight: 800; + font-stretch: 125%; + letter-spacing: -0.01em; +} +.mlbundle-wordmark .rest { + font-weight: 300; + font-stretch: 100%; +} +.app-brand-header a:focus-visible { + outline: 2px solid var(--text); + outline-offset: 4px; +} +.app-brand-header nav { + display: flex; + gap: 0.25rem; + margin-left: auto; +} +.app-brand-header nav .icon-button { + border-color: transparent; + background: transparent; + text-decoration: none; +} +.app-brand-header nav .icon-button:hover { + background: var(--hover-surface); +} +@media (max-width: 620px) { + :root { + --header-height: 4.35rem; + } + .app-brand-header { + gap: 0.65rem; + padding-inline: 4.6rem 0.75rem; + } + .app-brand-header nav { + gap: 0; + } + .mlbundle-brand { + gap: 0.35rem; + font-size: 1.5rem; + } + .mlbundle-brand img { + width: 1.4rem; + height: 2.15rem; + } +} +@media (max-width: 380px) { + .mlbundle-brand { + font-size: 1.3rem; + } +} diff --git a/playground/src/styles/index.css b/playground/src/styles/index.css new file mode 100644 index 0000000..4f6562a --- /dev/null +++ b/playground/src/styles/index.css @@ -0,0 +1,12 @@ +@import "./tokens.css"; +@import "./base.css"; +@import "./header.css"; +@import "./workspace.css"; +@import "./output.css"; +@import "./editor.css"; +@import "./controls.css"; +@import "./menu.css"; +@import "./viewport.css"; +@import "./themes.css"; +@import "./recipes.css"; +@import "./inspector.css"; diff --git a/playground/src/styles/inspector.css b/playground/src/styles/inspector.css new file mode 100644 index 0000000..37e6ab6 --- /dev/null +++ b/playground/src/styles/inspector.css @@ -0,0 +1,226 @@ +/* Bundle inspector: bundle bar, view tabs and the tab panel. */ +.output-banner { + flex-shrink: 0; + margin: 0; + padding: 0.6rem 0.8rem; + border-bottom: var(--border-width) solid var(--border); + color: var(--text-muted); + font-size: 0.8rem; +} + +.inspector-bar { + flex-shrink: 0; + display: flex; + flex-wrap: wrap; + align-items: center; + gap: 0.5rem; + min-height: 2.75rem; + padding: 0.35rem 0.75rem; + border-bottom: var(--border-width) solid var(--border); +} +.inspector-title { + flex: 1 1 8rem; + min-width: 0; + overflow: hidden; + font: 0.8rem var(--font-mono); + text-overflow: ellipsis; + white-space: nowrap; +} +.inspector-chip { + flex-shrink: 0; + border-radius: 999px; + padding: 0.1rem 0.55rem; + border: var(--border-width) solid currentColor; + font-size: 0.72rem; + font-weight: 600; +} +.inspector-chip[data-status="verified"] { color: var(--success); } +.inspector-chip[data-status="inspected"] { color: var(--info); } +.inspector-chip:is([data-status="unverified"], [data-status="rejected"]) { color: var(--danger); } + +.inspector-tabs { + flex-shrink: 0; + display: flex; + gap: 0.15rem; + overflow-x: auto; + padding: 0 0.5rem; + border-bottom: var(--border-width) solid var(--border); + scrollbar-width: thin; +} +.code-workspace .inspector-tabs [role="tab"] { + flex-shrink: 0; + min-height: 2.4rem; + border: 0; + border-bottom: 2px solid transparent; + border-radius: 0; + background: transparent; + color: var(--text-muted); + font-weight: 500; +} +.code-workspace .inspector-tabs [role="tab"][aria-selected="true"] { + border-bottom-color: var(--accent); + color: var(--text); +} +.inspector-tabs [role="tab"]:focus-visible, +.inspector-panel:focus-visible, +.inspector-table:focus-visible { + outline: 2px solid var(--accent); + outline-offset: -2px; +} + +.inspector-panel { + flex: 1; + min-height: 0; + overflow: auto; + padding: 0.75rem 0.9rem 1.5rem; + font-size: 0.85rem; +} +.inspector-panel.is-manifest { + display: flex; + padding: 0; +} +.inspector-panel.is-manifest .output-json { + flex: 1; +} + +.inspector-status { + display: grid; + gap: 0.25rem; + margin-bottom: 0.9rem; + padding: 0.65rem 0.8rem; + border-radius: var(--radius-sm); + border: var(--border-width) solid var(--border); + border-left: 3px solid var(--info); + background: var(--surface-muted); +} +.inspector-status[data-status="verified"] { border-left-color: var(--success); } +.inspector-status:is([data-status="unverified"], [data-status="rejected"]) { + border-left-color: var(--danger); + background: var(--danger-soft); +} +.inspector-status span { color: var(--text-muted); } +.inspector-error { + font: 0.78rem/1.5 var(--font-mono); + color: var(--danger); + overflow-wrap: anywhere; + white-space: pre-wrap; +} + +.inspector-facts { + display: grid; + grid-template-columns: max-content minmax(0, 1fr); + gap: 0.3rem 1rem; + margin: 0 0 1rem; +} +.inspector-facts dt { color: var(--text-muted); } +.inspector-facts dd { + margin: 0; + overflow-wrap: anywhere; + font-family: var(--font-mono); + font-size: 0.8rem; +} + +.inspector-section { margin-top: 1.1rem; } +.inspector-section h3, +.inspector-table caption { + margin: 0 0 0.4rem; + color: var(--text-muted); + font-size: 0.7rem; + font-weight: 600; + letter-spacing: 0.06em; + text-align: left; + text-transform: uppercase; +} +.inspector-note { + margin: 0 0 0.6rem; + color: var(--text-muted); + line-height: 1.5; +} + +.inspector-table { + max-width: 100%; + overflow-x: auto; + margin-bottom: 0.6rem; +} +.inspector-table table { + min-width: 100%; + border-collapse: collapse; + font-size: 0.8rem; +} +.inspector-table :is(th, td) { + padding: 0.3rem 0.6rem; + border-bottom: var(--border-width) solid var(--border); + text-align: left; + vertical-align: top; +} +.inspector-table td { + font-family: var(--font-mono); + overflow-wrap: anywhere; +} +.inspector-table th { color: var(--text-muted); font-weight: 600; } + +.inspector-bars td:last-child { width: 45%; } +.inspector-bar-fill { + display: block; + height: 0.6rem; + min-width: 1px; + border-radius: 2px; + background: var(--accent); +} +.inspector-bar-fill[data-sign="negative"] { background: var(--info); } + +.tree-outline, +.tree-outline ul { + margin: 0; + padding-left: 1.1rem; + list-style: none; + font: 0.78rem/1.7 var(--font-mono); +} +.tree-outline { padding-left: 0; margin-bottom: 0.8rem; } +.tree-outline ul { border-left: var(--border-width) solid var(--border); } +.tree-outline summary { cursor: pointer; } +.tree-outline summary:focus-visible { outline: 2px solid var(--accent); } +.tree-branch { + color: var(--accent); + font-size: 0.7rem; + text-transform: uppercase; +} +.tree-meta, +.tree-leaf { color: var(--text-muted); } +.tree-host { margin-top: 0.6rem; } + +.inspector-raw { margin-top: 1rem; } +.inspector-raw summary { cursor: pointer; color: var(--text-muted); } +.inspector-raw pre { + max-height: 24rem; + overflow: auto; + margin: 0.4rem 0 0; + padding: 0.6rem; + border-radius: var(--radius-sm); + background: var(--surface-muted); + font: 0.75rem/1.5 var(--font-mono); +} + +.output-console { + display: flex; + flex-direction: column; + gap: 0.6rem; +} +.output-console :is(.output-error, .output-repr, .output-streams) { + max-height: none; + margin: 0; + padding: 0.7rem; + border-radius: var(--radius-sm); + font: 0.78rem/1.55 var(--font-mono); + white-space: pre-wrap; + overflow-wrap: anywhere; +} +.output-console :is(.output-repr, .output-streams) { background: var(--surface-muted); } +.output-console .output-error { border: 0; } +.output-console .output-note { margin: 0; padding: 0; } + +@media (max-width: 620px) { + .inspector-panel { padding-inline: 0.6rem; } + .inspector-facts { grid-template-columns: minmax(0, 1fr); gap: 0 0; } + .inspector-facts dd { margin-bottom: 0.4rem; } +} diff --git a/playground/src/styles/menu.css b/playground/src/styles/menu.css new file mode 100644 index 0000000..5344523 --- /dev/null +++ b/playground/src/styles/menu.css @@ -0,0 +1,141 @@ +/* Floating options menu, as in MLForm's playground (demo-menu.css). */ +.app-menu { + position: fixed; + top: 1rem; + left: 1rem; + z-index: 50; +} + +.app-menu-button { + width: 3rem; + height: 3rem; + display: grid; + place-content: center; + padding: 0; + border: var(--border-width) solid var(--border-strong); + border-radius: var(--radius-xl); + background: var(--surface-elevated); + color: var(--text); + cursor: pointer; + transition: + transform var(--motion-fast), + background var(--motion-fast); +} +.app-menu-button::after { + right: auto; + left: 0; +} +.app-menu.is-open .app-menu-button::after { + display: none; +} + +.app-menu-button:hover, +.app-menu-button:focus-visible, +.app-menu.is-open .app-menu-button { + transform: translateY(-1px); + background: var(--hover-surface); +} + +.app-menu-button:focus-visible, +.app-menu-option:focus-visible, +.app-menu-field :is(select, input):focus-visible { + outline: 3px solid var(--text); + outline-offset: 3px; +} + +.app-menu-panel { + position: absolute; + top: calc(100% + 0.55rem); + left: 0; + width: min(18rem, calc(100vw - 2rem)); + max-height: calc(100dvh - 5rem); + overflow-y: auto; + padding: 0.75rem; + display: grid; + gap: 0.55rem; + border: var(--border-width) solid var(--border-strong); + border-radius: var(--radius-xl); + background: var(--chrome-bg); + color: var(--text); + box-shadow: var(--shadow); + transform-origin: top left; +} +.app-menu-panel[hidden] { + display: none; +} + +.app-menu-list { + display: grid; + gap: 0.2rem; +} + +.app-menu-section-label { + padding: 0 0.35rem; + color: var(--text-muted); + font-size: 0.78rem; + font-weight: 700; +} + +.app-menu-option { + width: 100%; + min-height: 2.35rem; + padding: 0.55rem 0.7rem; + border: 0; + border-radius: var(--radius-sm); + background: transparent; + color: var(--text); + text-align: left; + cursor: pointer; +} +.app-menu-option:hover, +.app-menu-option:focus-visible { + background: var(--hover-surface); +} +.app-menu-option.is-active { + background: var(--accent); + color: var(--text-inverse); + font-weight: 700; +} + +.app-menu-settings { + display: grid; + gap: 0.7rem; + padding: 0.8rem 0.35rem 0.2rem; + border-top: var(--border-width) solid var(--border); +} + +.app-menu-field { + display: grid; + gap: 0.25rem; + font-size: 0.85rem; + font-weight: 600; +} +.app-menu-field :is(select, input) { + width: 100%; + min-width: 0; + min-height: 2.35rem; + padding: 0.35rem 0.55rem; + border: var(--border-width) solid var(--border-strong); + border-radius: var(--radius-sm); + background: var(--surface); + color: var(--text); + font-weight: 400; + cursor: pointer; +} +.app-menu-field input { + cursor: text; +} + +.app-menu-custom-size { + display: grid; + grid-template-columns: repeat(2, minmax(0, 1fr)); + gap: 0.55rem; +} + +@media (forced-colors: active) { + .app-menu-option.is-active { + forced-color-adjust: none; + background: Highlight; + color: HighlightText; + } +} diff --git a/playground/src/styles/output.css b/playground/src/styles/output.css new file mode 100644 index 0000000..c344657 --- /dev/null +++ b/playground/src/styles/output.css @@ -0,0 +1,23 @@ +.code-output { + position: relative; +} +.output-json { + min-height: 6rem; + overflow: auto; +} +.code-output .output-note { + color: var(--text-muted); + font-family: var(--font-sans); + font-size: 0.8rem; +} +.output-error { + background: var(--danger-soft); + color: var(--danger); +} +/* Author display rules (flex, grid) must not override the hidden attribute. */ +[hidden] { + display: none !important; +} +.console-stderr { + color: var(--danger); +} diff --git a/playground/src/styles/recipes.css b/playground/src/styles/recipes.css new file mode 100644 index 0000000..1512f66 --- /dev/null +++ b/playground/src/styles/recipes.css @@ -0,0 +1,23 @@ +/* MLForm's style recipes, applied to the playground chrome. Default is tokens.css. */ +:root[data-style="minimal"] { + /* Compact density, no motion, no shadows. */ + --control-size: 2rem; + --motion-fast: 0ms linear; + --shadow: none; +} +:root[data-style="soft"] { + --chrome-bg: + radial-gradient(circle at top left, color-mix(in srgb, var(--accent) 10%, transparent), transparent 42%), + linear-gradient( + 180deg, + color-mix(in srgb, var(--surface) 92%, var(--surface-elevated)), + color-mix(in srgb, var(--surface-muted) 88%, var(--surface-elevated)) + ); +} +:root[data-style="contrast"] { + /* Spacious density with stronger borders. */ + --control-size: 2.5rem; + --border-width: 1.5px; + --border-strong: var(--text); + --border: var(--text); +} diff --git a/playground/src/styles/themes.css b/playground/src/styles/themes.css new file mode 100644 index 0000000..9ad27c7 --- /dev/null +++ b/playground/src/styles/themes.css @@ -0,0 +1,154 @@ +/* MLForm's other design systems (eshkra lives in tokens.css). Colors and radii only: + the playground keeps its own fonts and needs no form runtime. */ +:root[data-theme="m3diseen"] { + --radius-lg: 8px; + --radius-xl: 12px; + --bg: light-dark(#9092c7, #161722); + --surface: light-dark(#ffffff, #1e2030); + --surface-muted: light-dark(#f3f3f8, #252739); + --surface-elevated: light-dark(#ffffff, #2c2e42); + --text: light-dark(#212529, #f3f3fa); + --text-muted: light-dark(#54566c, #bfc1d7); + --text-inverse: light-dark(#ffffff, #161722); + --border: light-dark(#d9dae6, #3c3e55); + --border-strong: light-dark(#b9bbce, #555875); + --accent: light-dark(#5c5f9e, #b0b3e4); + --success: light-dark(#246549, #80d6a9); + --danger: light-dark(#a73342, #ffa2ad); + --danger-soft: light-dark(rgba(167, 51, 66, 0.08), rgba(255, 162, 173, 0.12)); + --hover-surface: light-dark(#e9e9f4, #383b53); +} +:root[data-theme="dosetailor"] { + --radius-lg: 8px; + --radius-xl: 12px; + --bg: light-dark(#a9a4e3, #191626); + --surface: light-dark(#ffffff, #221e33); + --surface-muted: light-dark(#f5f4fa, #2b2540); + --surface-elevated: light-dark(#ffffff, #322b48); + --text: light-dark(#212529, #f6f3ff); + --text-muted: light-dark(#5e5975, #c9c0de); + --text-inverse: light-dark(#ffffff, #191626); + --border: light-dark(#dfdceb, #463c5e); + --border-strong: light-dark(#beb8d4, #625578); + --accent: light-dark(#655dc6, #b7aaf7); + --success: light-dark(#246549, #80d6a9); + --danger: light-dark(#a73342, #ffa2ad); + --danger-soft: light-dark(rgba(167, 51, 66, 0.08), rgba(255, 162, 173, 0.12)); + --hover-surface: light-dark(#eeecfa, #403657); +} +:root[data-theme="clickhouse"] { + --radius-xl: 12px; + --bg: light-dark(#f7f7f2, #0a0a0a); + --surface: light-dark(#ffffff, #121212); + --surface-muted: light-dark(#eeeeea, #1a1a1a); + --surface-elevated: light-dark(#f4f4ef, #242424); + --text: light-dark(#0a0a0a, #ffffff); + --text-muted: light-dark(#5a5a5a, #cccccc); + --text-inverse: light-dark(#0a0a0a, #0a0a0a); + --border: light-dark(#d8d8cf, #2a2a2a); + --border-strong: light-dark(#bdbdaf, #3a3a3a); + --accent: light-dark(#e6eb52, #faff69); + --success: light-dark(#126d31, #22c55e); + --danger: light-dark(#b32d2d, #ff7770); + --danger-soft: light-dark(rgba(199, 57, 57, 0.12), rgba(239, 68, 68, 0.16)); + --hover-surface: light-dark(#eeeeea, #242424); +} +:root[data-theme="airbnb"] { + --radius-sm: 8px; + --radius-lg: 20px; + --radius-xl: 32px; + --bg: light-dark(#ffffff, #121212); + --surface: light-dark(#ffffff, #1c1c1c); + --surface-muted: light-dark(#f7f7f7, #262626); + --surface-elevated: light-dark(#ffffff, #2b2b2b); + --text: light-dark(#222222, #f7f7f7); + --text-muted: light-dark(#6a6a6a, #b0b0b0); + --text-inverse: light-dark(#222222, #121212); + --border: light-dark(#dddddd, #3a3a3a); + --border-strong: light-dark(#c1c1c1, #505050); + --accent: light-dark(#ff385c, #ff385c); + --success: light-dark(#007508, #2fbd59); + --danger: light-dark(#c13515, #ff6b5f); + --danger-soft: light-dark(rgba(193, 53, 21, 0.12), rgba(255, 107, 95, 0.16)); + --hover-surface: light-dark(#f2f2f2, #333333); +} +:root[data-theme="neutral"] { + --bg: light-dark(#f4f6f8, #12161c); + --surface: light-dark(#ffffff, #181d24); + --surface-muted: light-dark(#f0f3f6, #212832); + --surface-elevated: light-dark(#ffffff, #202731); + --text: light-dark(#172330, #ebf1f7); + --text-muted: light-dark(#526170, #a5b1bd); + --text-inverse: light-dark(#ffffff, #12161c); + --border: light-dark(#d9e1e7, #313a47); + --border-strong: light-dark(#b9c5cf, #414b58); + --accent: light-dark(#2456c7, #71a7ff); + --success: light-dark(#16704f, #42c88e); + --danger: light-dark(#b93636, #ff7a72); + --danger-soft: light-dark(rgba(185, 54, 54, 0.08), rgba(255, 122, 114, 0.14)); + --hover-surface: light-dark(#e8edf2, #2a3440); +} +:root[data-theme="cobalt"] { + --bg: light-dark(#eef4fb, #0d1524); + --surface: light-dark(#ffffff, #111c30); + --surface-muted: light-dark(#e5eef8, #16233b); + --surface-elevated: light-dark(#ffffff, #1a2944); + --text: light-dark(#11243a, #edf4ff); + --text-muted: light-dark(#49627d, #a3b5cf); + --text-inverse: light-dark(#ffffff, #0d1524); + --border: light-dark(#c6d7ea, #243552); + --border-strong: light-dark(#afc6dd, #304465); + --accent: light-dark(#1f5eff, #68a0ff); + --success: light-dark(#167347, #3ad38a); + --danger: light-dark(#bd3434, #ff7f86); + --danger-soft: light-dark(rgba(214, 69, 69, 0.12), rgba(255, 127, 134, 0.14)); + --hover-surface: light-dark(#d8e5f6, #1f3151); +} +:root[data-theme="sage"] { + --bg: light-dark(#f2f6f1, #121a15); + --surface: light-dark(#fbfdfb, #182119); + --surface-muted: light-dark(#e8efe6, #203024); + --surface-elevated: light-dark(#ffffff, #24372a); + --text: light-dark(#203126, #edf5ef); + --text-muted: light-dark(#526557, #a8b9aa); + --text-inverse: light-dark(#ffffff, #121a15); + --border: light-dark(#cfdbcf, #304234); + --border-strong: light-dark(#bbccbc, #3f5544); + --accent: light-dark(#2f7d57, #59b07d); + --success: light-dark(#256c44, #67d595); + --danger: light-dark(#a83f3d, #ff8e8a); + --danger-soft: light-dark(rgba(185, 74, 72, 0.12), rgba(255, 142, 138, 0.14)); + --hover-surface: light-dark(#dde9db, #2a3d30); +} +:root[data-theme="sunset"] { + --bg: light-dark(#fff6f0, #1e1512); + --surface: light-dark(#fffdfb, #261b18); + --surface-muted: light-dark(#ffe9dd, #32231f); + --surface-elevated: light-dark(#ffffff, #392925); + --text: light-dark(#3b241d, #fff1ea); + --text-muted: light-dark(#7f5a50, #d3b0a3); + --text-inverse: light-dark(#26140e, #1e1512); + --border: light-dark(#f0cdbb, #513830); + --border-strong: light-dark(#e0b59f, #68473d); + --accent: light-dark(#d96a3a, #ff986b); + --success: light-dark(#25734f, #57c694); + --danger: light-dark(#b43e38, #ff8d86); + --danger-soft: light-dark(rgba(202, 74, 69, 0.12), rgba(255, 141, 134, 0.14)); + --hover-surface: light-dark(#ffdcca, #422d27); +} +:root[data-theme="graphite"] { + --bg: light-dark(#f1f4f7, #0f1318); + --surface: light-dark(#ffffff, #171c22); + --surface-muted: light-dark(#e6ebf1, #202731); + --surface-elevated: light-dark(#ffffff, #24303d); + --text: light-dark(#202934, #ebf1f7); + --text-muted: light-dark(#5d6977, #9ca8b5); + --text-inverse: light-dark(#ffffff, #0f1318); + --border: light-dark(#cfd7e1, #2c3642); + --border-strong: light-dark(#bbc6d3, #3b4754); + --accent: light-dark(#375a7f, #4ea1ff); + --success: light-dark(#176c47, #37c78b); + --danger: light-dark(#be3a34, #ff6a61); + --danger-soft: light-dark(rgba(190, 58, 52, 0.12), rgba(255, 106, 97, 0.14)); + --hover-surface: light-dark(#dbe2ea, #24303d); +} diff --git a/playground/src/styles/tokens.css b/playground/src/styles/tokens.css new file mode 100644 index 0000000..1700091 --- /dev/null +++ b/playground/src/styles/tokens.css @@ -0,0 +1,46 @@ +/* The eshkra design system, MLForm's default theme; themes.css holds the others. + light-dark() follows color-scheme: the system's, or the visitor's choice. */ +:root { + color-scheme: light dark; + --font-sans: "Mona Sans Variable", "Mona Sans", ui-sans-serif, system-ui, sans-serif; + --font-mono: "JetBrains Mono Variable", "JetBrains Mono", ui-monospace, monospace; + --radius-sm: 6px; + --radius-lg: 12px; + --radius-xl: 14px; + --border-width: 1px; + --motion-fast: 120ms ease; + --shadow: 0 10px 30px rgb(0 0 0 / 0.08); + --control-size: 2.25rem; + --code-font-size: 0.8rem; + --code-line-height: 1.6; + --header-height: 4.75rem; + + --bg: light-dark(#ffffff, #0a0a0d); + --surface: light-dark(#ffffff, #131318); + --surface-muted: light-dark(#f7f7f9, #1a1a21); + --surface-elevated: light-dark(#ffffff, #1c1c24); + --text: light-dark(#0b0b10, #f4f4f6); + --text-muted: light-dark(#666675, #b6b6c2); + --text-inverse: light-dark(#ffffff, #0a0a0d); + --border: light-dark(#e7e7ee, #24242c); + --border-strong: light-dark(#d2d2dc, #34343e); + /* The ink shade keeps small orange text readable on white. */ + --accent: light-dark(#c2410c, #ff9a55); + --success: light-dark(#16704f, #65d2a1); + --info: light-dark(#3b4fc4, #9aa8ff); + --danger: light-dark(#b93636, #ff8e8a); + --danger-soft: light-dark(rgba(185, 54, 54, 0.08), rgba(255, 142, 138, 0.12)); + --hover-surface: light-dark(#fff0e8, #29221f); + /* Derived roles follow whichever design system sets the palette. */ + --button-bg: var(--text); + --button-text: var(--surface); + --chrome-bg: var(--surface-elevated); +} + +:root[data-appearance="light"] { + color-scheme: light; +} + +:root[data-appearance="dark"] { + color-scheme: dark; +} diff --git a/playground/src/styles/viewport.css b/playground/src/styles/viewport.css new file mode 100644 index 0000000..eca0c97 --- /dev/null +++ b/playground/src/styles/viewport.css @@ -0,0 +1,51 @@ +/* The editor frame fills the stage, or simulates a device size as in MLForm's preview.css. */ +.app-stage { + flex: 1; + min-width: 0; + min-height: 0; + overflow: hidden; +} +.app-preview, +.app-preview-viewport, +.app-preview-frame { + width: 100%; + height: 100%; +} +.app-preview-frame { + display: block; + border: 0; + background: var(--bg); +} + +.app-stage.is-preview { + overflow: auto; + padding: 0 1rem; + border-top: var(--border-width) solid var(--border); +} +.is-preview .app-preview { + display: grid; + justify-items: center; + align-content: start; + gap: 0.55rem; + height: auto; + padding-block: 0.75rem 1rem; +} +.is-preview .app-preview-label { + color: var(--text-muted); + font: 700 0.82rem/1.5 var(--font-mono); + font-variant-ligatures: none; + text-align: center; +} +.is-preview .app-preview-viewport { + position: relative; + overflow: hidden; + border: var(--border-width) solid var(--border-strong); + border-radius: var(--radius-lg); + box-sizing: content-box; +} +.is-preview .app-preview-frame { + position: absolute; + top: 0; + left: 0; + transform-origin: top left; +} diff --git a/playground/src/styles/workspace.css b/playground/src/styles/workspace.css new file mode 100644 index 0000000..3977922 --- /dev/null +++ b/playground/src/styles/workspace.css @@ -0,0 +1,210 @@ +.code-workspace { + display: flex; + flex-direction: column; + min-width: 0; + min-height: 0; +} + +.code-toolbar { + /* Above the panes, so control tooltips can overlap them. */ + position: relative; + z-index: 5; + flex-shrink: 0; + display: flex; + flex-wrap: wrap; + align-items: center; + gap: 0.35rem; + min-height: 2.75rem; + padding: 0.35rem 0.75rem; + border-block: var(--border-width) solid var(--border); + background: var(--chrome-bg); +} + +.code-select { + display: flex; + min-width: 0; +} +.code-select select { + max-width: 100%; + min-width: 0; +} +.code-position { + margin-right: auto; +} + +.code-workspace button, +.code-workspace select { + min-height: var(--control-size); + border: var(--border-width) solid var(--border-strong); + border-radius: var(--radius-sm); + padding: 0.3rem 0.6rem; + background: var(--surface); + color: var(--text); + font-size: 0.85rem; + cursor: pointer; +} +.code-workspace button:hover:not(:disabled) { + background: var(--hover-surface); +} +.code-workspace :is(button, select):disabled { + cursor: not-allowed; + opacity: 0.55; +} + +.code-workspace .code-run { + display: inline-flex; + align-items: center; + justify-content: center; + gap: 0.4rem; + border-color: var(--button-bg); + background: var(--button-bg); + color: var(--button-text); + font-weight: 600; +} +.code-workspace .code-run:hover:not(:disabled) { + border-color: var(--accent); + background: var(--accent); + color: var(--text-inverse); +} + +.code-status { + display: grid; + place-items: center; + width: var(--control-size); + height: var(--control-size); + flex-shrink: 0; + color: var(--text-muted); +} +.code-status[data-symbol="check"] { color: var(--success); } +.code-status[data-symbol="error"] { color: var(--danger); } +.code-status[data-symbol="loading"] svg { animation: status-spin 1s linear infinite; } +@keyframes status-spin { to { transform: rotate(360deg); } } +@media (prefers-reduced-motion: reduce) { + .code-status[data-symbol="loading"] svg { animation: none; } +} + +.code-notice { + flex-basis: 100%; + margin: 0; + font-size: 0.8rem; +} + +.code-split { + flex: 1; + display: grid; + min-width: 0; + min-height: 0; + grid-template-columns: minmax(0, var(--code-share)) 0.75rem minmax(0, var(--output-share)); + grid-template-rows: minmax(0, 1fr); +} +.code-source { + grid-column: 1; + grid-row: 1; +} +.code-separator { + grid-column: 2; + grid-row: 1; +} +.code-output { + grid-column: 3; + grid-row: 1; +} +.code-split[data-position="right"] { + grid-template-columns: minmax(0, var(--output-share)) 0.75rem minmax(0, var(--code-share)); +} +.code-split[data-position="right"] .code-source { + grid-column: 3; +} +.code-split[data-position="right"] .code-output { + grid-column: 1; +} +.code-split:is([data-position="top"], [data-position="bottom"]) { + grid-template-columns: minmax(0, 1fr); + grid-template-rows: minmax(0, var(--code-share)) 0.75rem minmax(0, var(--output-share)); +} +.code-split:is([data-position="top"], [data-position="bottom"]) > * { + grid-column: 1; +} +.code-split:is([data-position="top"], [data-position="bottom"]) .code-output { + grid-row: 3; +} +.code-split:is([data-position="top"], [data-position="bottom"]) .code-separator { + grid-row: 2; + cursor: row-resize; +} +.code-split[data-position="bottom"] { + grid-template-rows: minmax(0, var(--output-share)) 0.75rem minmax(0, var(--code-share)); +} +.code-split[data-position="bottom"] .code-source { + grid-row: 3; +} +.code-split[data-position="bottom"] .code-output { + grid-row: 1; +} + +.code-pane { + display: flex; + flex-direction: column; + min-width: 0; + min-height: 0; + overflow: hidden; + background: var(--surface); +} +.code-editor { + flex: 1; + min-height: 0; + overflow: hidden; +} + +.code-separator { + display: grid; + place-items: center; + cursor: col-resize; + touch-action: none; + background: var(--bg); + border-inline: var(--border-width) solid var(--border); +} +.code-separator::after { + content: ""; + width: 2px; + height: 2rem; + border-radius: 2px; + background: var(--border-strong); +} +.code-separator:hover::after, +.code-separator:focus-visible::after { + background: var(--accent); +} +.code-separator[aria-orientation="horizontal"] { + border-inline: 0; + border-block: var(--border-width) solid var(--border); +} +.code-separator[aria-orientation="horizontal"]::after { + width: 2rem; + height: 2px; +} +.code-split.is-resizing { + user-select: none; +} + +@media (max-width: 620px) { + .code-toolbar { + padding-inline: 0.4rem; + } + .code-select:not(.code-position) { + flex: 1 1 100%; + } + .code-select:not(.code-position) select { + width: 100%; + } + .code-position { + margin-right: auto; + flex: 1; + } +} + +@media (forced-colors: active) { + .code-separator::after { + background: ButtonText; + } +} diff --git a/playground/src/viewport.ts b/playground/src/viewport.ts new file mode 100644 index 0000000..661e3d6 --- /dev/null +++ b/playground/src/viewport.ts @@ -0,0 +1,53 @@ +import { applyDesign, viewportSize, type Settings } from "./settings.ts"; + +/** + * The editor always runs in one same-origin frame. Viewport changes only resize or scale + * that frame, so the code, the output and the running Python survive every change. + */ +export const mountViewport = (stage: HTMLElement, initial: Settings) => { + let settings = initial; + stage.innerHTML = `
+ +
+
`; + const preview = stage.querySelector(".app-preview")!; + const label = stage.querySelector(".app-preview-label")!; + const viewport = stage.querySelector(".app-preview-viewport")!; + const frame = stage.querySelector("iframe")!; + const syncDesign = () => { + const root = frame.contentDocument?.documentElement; + if (root) applyDesign(root, settings); + }; + const resize = () => { + const size = viewportSize(settings); + stage.classList.toggle("is-preview", size !== null); + label.hidden = size === null; + if (!size) { + viewport.removeAttribute("style"); + frame.removeAttribute("style"); + return; + } + const { width, height, name } = size; + // Scale down to fit the stage width, as MLForm's preview does; height may scroll. + const room = preview.clientWidth - (viewport.offsetWidth - viewport.clientWidth); + const scale = Math.min(1, Math.max(1, room) / width); + viewport.style.width = `${width * scale}px`; + viewport.style.height = `${height * scale}px`; + frame.style.width = `${width}px`; + frame.style.height = `${height}px`; + frame.style.transform = scale < 1 ? `scale(${scale})` : ""; + label.textContent = `${name} · ${width} × ${height} px${scale < 1 ? ` · ${Math.round(scale * 100)} %` : ""}`; + }; + frame.addEventListener("load", syncDesign); + const url = new URL(window.location.href); + url.search = "?editor=1"; + url.hash = ""; + frame.src = url.href; + new ResizeObserver(resize).observe(stage); + resize(); + return (next: Settings) => { + settings = next; + syncDesign(); + resize(); + }; +}; diff --git a/playground/src/workspace/editor.ts b/playground/src/workspace/editor.ts new file mode 100644 index 0000000..b789bf6 --- /dev/null +++ b/playground/src/workspace/editor.ts @@ -0,0 +1,69 @@ +import { defaultKeymap, history, historyKeymap, indentWithTab, isolateHistory } from "@codemirror/commands"; +import { python } from "@codemirror/lang-python"; +import { bracketMatching, indentOnInput, indentUnit } from "@codemirror/language"; +import { EditorState } from "@codemirror/state"; +import { EditorView, highlightActiveLine, keymap, lineNumbers } from "@codemirror/view"; +import { codeHighlighting } from "./highlight.ts"; + +export const createCodeEditor = ( + parent: HTMLElement, + code: string, + onChange: (value: string) => void, + onRun: () => void, +) => { + const editor = new EditorView({ + parent, + state: EditorState.create({ + doc: code, + extensions: [ + lineNumbers(), + history(), + highlightActiveLine(), + indentOnInput(), + indentUnit.of(" "), + bracketMatching(), + python(), + codeHighlighting, + // Escape then Tab leaves the editor, so indenting with Tab does not trap focus. + keymap.of([...defaultKeymap, ...historyKeymap, indentWithTab]), + EditorView.contentAttributes.of({ + "aria-label": "Python code", + spellcheck: "false", + autocorrect: "off", + autocapitalize: "off", + }), + EditorView.updateListener.of((update) => { + if (update.docChanged) onChange(update.state.doc.toString()); + }), + ], + }), + }); + const runShortcut = (event: KeyboardEvent) => { + if (event.key !== "Enter" || !(event.ctrlKey || event.metaKey) || event.altKey) return; + if (event.isComposing) return; + // Handle this app command before CodeMirror's Android Enter synthesis drops modifiers. + event.preventDefault(); + event.stopPropagation(); + onRun(); + }; + editor.contentDOM.addEventListener("keydown", runShortcut, true); + return { + setCode(value: string) { + editor.dispatch({ + ...(value !== editor.state.doc.toString() + ? { + changes: { from: 0, to: editor.state.doc.length, insert: value }, + annotations: isolateHistory.of("full"), + } + : {}), + selection: { anchor: 0 }, + effects: EditorView.scrollIntoView(0, { y: "start" }), + }); + }, + focus: () => editor.focus(), + destroy() { + editor.contentDOM.removeEventListener("keydown", runShortcut, true); + editor.destroy(); + }, + }; +}; diff --git a/playground/src/workspace/examples.ts b/playground/src/workspace/examples.ts new file mode 100644 index 0000000..02163eb --- /dev/null +++ b/playground/src/workspace/examples.ts @@ -0,0 +1,28 @@ +const sources = import.meta.glob("../examples/*.py", { + query: "?raw", + import: "default", + eager: true, +}); + +export interface CodeExample { + id: string; + label: string; + code: string; +} + +const CATALOG = [ + ["classifier", "Classifier with schema"], + ["regressor", "Random forest regressor"], + ["samples", "Explicit samples and one-hot"], + ["pipeline", "sklearn Pipeline"], + ["errors", "Rejected models"], +] as const; + +export const CODE_EXAMPLES: readonly CodeExample[] = CATALOG.map(([id, label]) => { + const code = sources[`../examples/${id}.py`]; + if (code === undefined) throw new Error(`Missing example file: ${id}.py`); + return { id, label, code }; +}); + +export const getCodeExample = (id: unknown): CodeExample => + CODE_EXAMPLES.find((example) => example.id === id) ?? CODE_EXAMPLES[0]!; diff --git a/playground/src/workspace/highlight.ts b/playground/src/workspace/highlight.ts new file mode 100644 index 0000000..bedddde --- /dev/null +++ b/playground/src/workspace/highlight.ts @@ -0,0 +1,12 @@ +import { HighlightStyle, syntaxHighlighting } from "@codemirror/language"; +import { tags } from "@lezer/highlight"; + +export const codeHighlighting = syntaxHighlighting( + HighlightStyle.define([ + { tag: [tags.keyword, tags.controlKeyword], color: "var(--accent)", fontWeight: "600" }, + { tag: [tags.string, tags.number, tags.bool, tags.null], color: "var(--success)" }, + { tag: tags.comment, color: "var(--text-muted)", fontStyle: "italic" }, + { tag: [tags.propertyName, tags.definition(tags.variableName)], color: "var(--info)" }, + { tag: [tags.className, tags.typeName], color: "var(--accent)", fontStyle: "italic" }, + ]), +); diff --git a/playground/src/workspace/mount.ts b/playground/src/workspace/mount.ts new file mode 100644 index 0000000..8f9381a --- /dev/null +++ b/playground/src/workspace/mount.ts @@ -0,0 +1,215 @@ +import { icon, labelIcon } from "../icons.ts"; +import { formatBytes } from "../inspector/dom.ts"; +import { createPythonRuntime, type RunResult, type RuntimeState } from "../runtime/client.ts"; +import { RUN_TIMEOUT_MS, UPLOAD_LIMIT_BYTES } from "../runtime/versions.ts"; +import { createCodeEditor } from "./editor.ts"; +import { CODE_EXAMPLES, getCodeExample } from "./examples.ts"; +import { createOutputPanel } from "./output.ts"; +import { bindCodeSplit } from "./split.ts"; +import { isCodePosition, readWorkspaceState, saveWorkspaceState } from "./state.ts"; +import { createStatus } from "./status.ts"; + +const MARKUP = ` +
+ + + + + + + + + +
+
+
+
+
+ +
+
+`; + +const failedRead = (text: string): RunResult => ({ + kind: "finished", + durationMs: 0, + report: { outcome: { kind: "error", text }, bundles: [], skipped: 0 }, +}); + +export const mountWorkspace = (container: HTMLElement, storage: Storage | undefined) => { + const first = CODE_EXAMPLES[0]!; + let state = readWorkspaceState(storage, { + code: first.code, + exampleId: first.id, + // Narrow screens start stacked; the position control still offers every layout. + position: window.innerWidth < 720 ? "top" : "left", + ratio: 50, + }); + let runtimeState: RuntimeState = { status: "booting", message: "Starting Python…" }; + let autoRun = true; + + const shell = document.createElement("section"); + shell.className = "code-workspace"; + shell.setAttribute("aria-label", "Python code and bundle inspector"); + shell.innerHTML = MARKUP; + container.replaceChildren(shell); + const part = (role: string) => + shell.querySelector(`[data-role="${role}"]`)!; + const exampleSelect = part("example"); + const positionSelect = part("position"); + const runButton = part("run"); + const uploadButton = part("upload"); + const fileInput = part("file"); + const status = createStatus(part("status")); + const notice = part("notice"); + exampleSelect.replaceChildren(...CODE_EXAMPLES.map(({ id, label }) => new Option(label, id))); + exampleSelect.value = getCodeExample(state.exampleId).id; + positionSelect.value = state.position; + const output = createOutputPanel(shell.querySelector(".code-output")!); + + const save = () => { + if (saveWorkspaceState(storage, state)) return; + notice.hidden = false; + notice.textContent = "Saving in this browser is unavailable. Copy your code before leaving."; + }; + const renderRunButton = () => { + const running = runtimeState.status === "running"; + if (running) labelIcon(runButton, "stop", "Stop"); + else labelIcon(runButton, "run", "Run", "Run (Ctrl/⌘ + Enter)"); + runButton.setAttribute("aria-description", running + ? "Stop this run. Python restarts." + : `Run the code (Ctrl/⌘ + Enter). Runs stop after ${RUN_TIMEOUT_MS / 1000} seconds.`); + runButton.disabled = runtimeState.status === "failed"; + runButton.setAttribute("aria-busy", String(running)); + // A new example would otherwise show the result of the code still running. + exampleSelect.disabled = running; + part("reset").disabled = running; + uploadButton.disabled = runtimeState.status !== "ready"; + }; + + const run = async () => { + if (runtimeState.status === "running") return; + if (runtimeState.status !== "ready") { + autoRun = runtimeState.status === "booting"; + if (autoRun) status.show("Will run when Python is ready", "loading"); + return; + } + output.begin(); + const result = await python.run(state.code); + output.finish(result, "run"); + if (result.kind === "finished") { + const failed = result.report.outcome.kind === "error"; + status.show(failed ? "Error" : "Done", failed ? "error" : "check"); + } else if (result.kind === "timeout") status.show("Timed out", "error"); + else if (result.kind === "cancelled") status.show("Stopped", "stop"); + }; + + const inspectFile = async () => { + const file = fileInput.files?.[0]; + fileInput.value = ""; + if (!file || runtimeState.status !== "ready") return; + output.begin(); + if (file.size > UPLOAD_LIMIT_BYTES) { + // Refused before reading, so a huge file never enters the tab's memory. + output.finish(failedRead(`${file.name} is ${formatBytes(file.size)}. The playground inspects files up to ${formatBytes(UPLOAD_LIMIT_BYTES)}; use the mlbundle CLI for larger bundles.`), "inspect"); + status.show("File too large", "error"); + return; + } + let data: ArrayBuffer; + try { + data = await file.arrayBuffer(); + } catch { + output.finish(failedRead(`${file.name} could not be read.`), "inspect"); + status.show("Error", "error"); + return; + } + const result = await python.inspect(file.name, data); + output.finish(result, "inspect"); + if (result.kind === "finished") { + const rejected = result.report.bundles[0]?.status !== "inspected"; + status.show(rejected ? "Rejected" : "Inspected", rejected ? "error" : "check"); + } else if (result.kind === "timeout") status.show("Timed out", "error"); + }; + + const python = createPythonRuntime( + () => new Worker(new URL("../runtime/worker.ts", import.meta.url), { type: "module" }), + { + onState(next) { + runtimeState = next; + renderRunButton(); + if (next.status === "booting") { + status.show(next.message, "loading"); + output.showBooting(next.message); + } else if (next.status === "failed") { + status.show("Python unavailable", "error"); + output.showFailure(next.message); + } else if (next.status === "running") status.show("Running…", "loading"); + else { + output.showReady(); + if (!["Done", "Error", "Inspected", "Rejected", "File too large"].includes(status.label)) { + status.show("Ready"); + } + if (autoRun) { + autoRun = false; + void run(); + } + } + }, + onStream: (stream, text) => output.stream(stream, text), + }, + ); + + const editor = createCodeEditor( + shell.querySelector(".code-editor")!, + state.code, + (code) => { + state = { ...state, code }; + save(); + }, + () => void run(), + ); + const split = bindCodeSplit( + shell.querySelector(".code-split")!, + shell.querySelector(".code-separator")!, + state.position, + state.ratio, + (position, ratio) => { + state = { ...state, position, ratio }; + save(); + }, + ); + + const loadExample = (id: string) => { + const example = getCodeExample(id); + state = { ...state, exampleId: example.id, code: example.code }; + editor.setCode(example.code); + save(); + void run(); + }; + runButton.addEventListener("click", () => { + if (runtimeState.status === "running") python.cancel(); + else void run(); + }); + exampleSelect.addEventListener("change", () => loadExample(exampleSelect.value)); + part("reset").addEventListener("click", () => { + loadExample(state.exampleId); + editor.focus(); + }); + part("restart").addEventListener("click", () => { + autoRun = false; + python.restart(); + }); + uploadButton.addEventListener("click", () => fileInput.click()); + fileInput.addEventListener("change", () => void inspectFile()); + positionSelect.addEventListener("change", () => { + if (isCodePosition(positionSelect.value)) split.setPosition(positionSelect.value); + }); + renderRunButton(); +}; diff --git a/playground/src/workspace/output.ts b/playground/src/workspace/output.ts new file mode 100644 index 0000000..4ba7257 --- /dev/null +++ b/playground/src/workspace/output.ts @@ -0,0 +1,92 @@ +import { createInspector } from "../inspector/panel.ts"; +import { h } from "../inspector/dom.ts"; +import type { RunResult } from "../runtime/client.ts"; +import type { OutputStream, RunReport } from "../runtime/protocol.ts"; + +const RESTART_HINT = "Check your connection, then choose Restart Python."; +const NO_BUNDLE = + "This run wrote no .mlbundle file. Call export(model, \"name.mlbundle\", …) to write one into the run's working directory."; + +/** + * The right pane: the bundle inspector, with the run's console as its last tab. + * The console holds errors, the last expression and print() output. + */ +export const createOutputPanel = (pane: HTMLElement) => { + const error = h("pre", { class: "output-error", role: "alert", hidden: "" }); + const message = h("p", { class: "output-note", role: "status" }); + const result = h("pre", { class: "output-repr", tabindex: "0", "aria-label": "Last expression", hidden: "" }); + const streams = h("pre", { class: "output-streams", tabindex: "0", "aria-label": "Console output", hidden: "" }); + const consoleView = h("div", { class: "output-console" }, error, message, result, streams); + const notice = h("p", { class: "output-banner", role: "status", hidden: "" }); + pane.append(notice); + const inspector = createInspector(pane, consoleView); + let bootFailed = false; + + const setText = (element: HTMLElement, text: string) => { + element.hidden = !text; + element.textContent = text; + }; + const showFailure = (text: string) => { + setText(error, `${text}\n${RESTART_HINT}`); + setText(notice, "Python is unavailable. See Console."); + bootFailed = true; + inspector.show("console"); + }; + const showReport = (report: RunReport, label: string) => { + const { outcome } = report; + setText(error, outcome.kind === "error" ? outcome.text : ""); + setText(result, outcome.kind === "json" || outcome.kind === "repr" ? outcome.text : ""); + const skipped = report.skipped ? ` ${report.skipped} more bundle(s) were not inspected (limit 8 per run).` : ""; + setText(message, `${label}${skipped}`); + inspector.setBundles(report.bundles, outcome.kind === "error" ? "The code raised an error before writing a bundle. See Console." : NO_BUNDLE); + if (outcome.kind === "error" && report.bundles.length === 0) inspector.show("console"); + else if (inspector.active === "console") inspector.show("overview"); + }; + + return { + begin() { + streams.replaceChildren(); + streams.hidden = true; + setText(notice, ""); + pane.setAttribute("aria-busy", "true"); + }, + stream(stream: OutputStream, text: string) { + streams.append(h("span", { class: `console-${stream}` }, text)); + streams.hidden = false; + }, + finish(run: RunResult, action: "run" | "inspect") { + pane.setAttribute("aria-busy", "false"); + if (run.kind === "timeout") { + setText(error, `Stopped after ${run.limitMs / 1000} seconds. Python restarted, so variables and imports were reset.`); + setText(result, ""); + inspector.setBundles([], "The run timed out before finishing."); + inspector.show("console"); + } else if (run.kind === "cancelled") { + setText(error, ""); + setText(message, "Stopped. Python restarted, so variables and imports were reset."); + inspector.setBundles([], "The run was stopped."); + } else if (run.kind === "failed") { + showFailure(run.message); + } else if (run.kind === "unavailable") { + setText(notice, "Python is not ready yet."); + } else { + const seconds = (run.durationMs / 1000).toFixed(1); + const label = action === "inspect" ? `Inspected in ${seconds} s with mlbundle.inspect.` : `Finished in ${seconds} s.`; + if (run.report.outcome.kind === "none" && action === "run") setText(result, ""); + showReport(run.report, label); + } + }, + /** A startup failure stays visible until Python boots again. */ + showFailure, + showBooting(text: string) { + if (bootFailed) { + bootFailed = false; + setText(error, ""); + } + setText(notice, `${text} The first visit downloads Python, scikit-learn and pandas; later visits use your browser cache.`); + }, + showReady() { + if (!bootFailed) setText(notice, ""); + }, + }; +}; diff --git a/playground/src/workspace/split.ts b/playground/src/workspace/split.ts new file mode 100644 index 0000000..026f6b5 --- /dev/null +++ b/playground/src/workspace/split.ts @@ -0,0 +1,90 @@ +import { clampRatio, type CodePosition } from "./state.ts"; + +export const bindCodeSplit = ( + split: HTMLElement, + separator: HTMLElement, + initialPosition: CodePosition, + initialRatio: number, + onChange: (position: CodePosition, ratio: number) => void, +) => { + let position = initialPosition; + let ratio = initialRatio; + let pointer: number | null = null; + const horizontal = () => position === "left" || position === "right"; + const reversed = () => position === "right" || position === "bottom"; + const update = () => { + ratio = clampRatio(ratio); + split.dataset.position = position; + split.style.setProperty("--code-share", `${ratio}fr`); + split.style.setProperty("--output-share", `${100 - ratio}fr`); + separator.setAttribute("aria-orientation", horizontal() ? "vertical" : "horizontal"); + separator.setAttribute("aria-valuenow", String(Math.round(ratio))); + separator.setAttribute("aria-valuetext", `Code: ${Math.round(ratio)} %`); + }; + const move = (event: PointerEvent) => { + if (pointer !== event.pointerId) return; + const bounds = split.getBoundingClientRect(); + const offset = horizontal() ? event.clientX - bounds.left : event.clientY - bounds.top; + const size = horizontal() ? bounds.width : bounds.height; + const divider = horizontal() ? separator.offsetWidth : separator.offsetHeight; + ratio = ((offset - divider / 2) / Math.max(1, size - divider)) * 100; + if (reversed()) ratio = 100 - ratio; + update(); + }; + const end = () => { + if (pointer === null) return; + pointer = null; + split.classList.remove("is-resizing"); + onChange(position, ratio); + }; + const start = (event: PointerEvent) => { + if (event.button !== 0) return; + event.preventDefault(); + pointer = event.pointerId; + separator.focus(); + separator.setPointerCapture(pointer); + split.classList.add("is-resizing"); + }; + const keydown = (event: KeyboardEvent) => { + const less = horizontal() ? "ArrowLeft" : "ArrowUp"; + const more = horizontal() ? "ArrowRight" : "ArrowDown"; + if (![less, more, "Home", "End", "Enter"].includes(event.key)) return; + event.preventDefault(); + if (event.key === "Home") ratio = 20; + else if (event.key === "End") ratio = 80; + else if (event.key === "Enter") ratio = 50; + else ratio += (event.key === more ? 1 : -1) * (reversed() ? -1 : 1) * (event.shiftKey ? 10 : 2); + update(); + onChange(position, ratio); + }; + const reset = () => { + ratio = 50; + update(); + onChange(position, ratio); + }; + separator.addEventListener("pointerdown", start); + separator.addEventListener("pointermove", move); + separator.addEventListener("pointerup", end); + separator.addEventListener("pointercancel", end); + separator.addEventListener("lostpointercapture", end); + separator.addEventListener("keydown", keydown); + separator.addEventListener("dblclick", reset); + update(); + return { + setPosition(next: CodePosition, nextRatio = ratio) { + position = next; + ratio = nextRatio; + update(); + onChange(position, ratio); + }, + destroy() { + separator.removeEventListener("pointerdown", start); + separator.removeEventListener("pointermove", move); + separator.removeEventListener("pointerup", end); + separator.removeEventListener("pointercancel", end); + separator.removeEventListener("lostpointercapture", end); + separator.removeEventListener("keydown", keydown); + separator.removeEventListener("dblclick", reset); + }, + }; +}; diff --git a/playground/src/workspace/state.ts b/playground/src/workspace/state.ts new file mode 100644 index 0000000..f40e9e3 --- /dev/null +++ b/playground/src/workspace/state.ts @@ -0,0 +1,50 @@ +export type CodePosition = "left" | "right" | "top" | "bottom"; +export interface WorkspaceState { + code: string; + exampleId: string; + position: CodePosition; + ratio: number; +} + +const STORAGE_KEY = "mlbundle-playground-workspace"; +export const isCodePosition = (value: unknown): value is CodePosition => + value === "left" || value === "right" || value === "top" || value === "bottom"; + +export const clampRatio = (ratio: number) => Math.max(20, Math.min(80, ratio)); + +export const readWorkspaceState = ( + storage: Pick | undefined, + fallback: WorkspaceState, +): WorkspaceState => { + try { + const saved: unknown = JSON.parse(storage?.getItem(STORAGE_KEY) ?? "null"); + if (!saved || typeof saved !== "object") return fallback; + return { + code: "code" in saved && typeof saved.code === "string" ? saved.code : fallback.code, + exampleId: + "exampleId" in saved && typeof saved.exampleId === "string" + ? saved.exampleId + : fallback.exampleId, + position: + "position" in saved && isCodePosition(saved.position) ? saved.position : fallback.position, + ratio: + "ratio" in saved && typeof saved.ratio === "number" && Number.isFinite(saved.ratio) + ? clampRatio(saved.ratio) + : fallback.ratio, + }; + } catch { + return fallback; + } +}; + +export const saveWorkspaceState = ( + storage: Pick | undefined, + state: WorkspaceState, +): boolean => { + try { + storage?.setItem(STORAGE_KEY, JSON.stringify(state)); + return storage !== undefined; + } catch { + return false; + } +}; diff --git a/playground/src/workspace/status.ts b/playground/src/workspace/status.ts new file mode 100644 index 0000000..dae56aa --- /dev/null +++ b/playground/src/workspace/status.ts @@ -0,0 +1,15 @@ +import { icon, type Icon } from "../icons.ts"; + +export const createStatus = (element: HTMLElement) => { + let label = ""; + return { + get label() { return label; }, + show(text: string, symbol: Icon = "ready") { + label = text; + element.innerHTML = `${icon(symbol)}`; + element.querySelector("span")!.textContent = text; + element.dataset.tooltip = text; + element.dataset.symbol = symbol; + }, + }; +}; diff --git a/playground/src/workspace/viewer.ts b/playground/src/workspace/viewer.ts new file mode 100644 index 0000000..24f558d --- /dev/null +++ b/playground/src/workspace/viewer.ts @@ -0,0 +1,29 @@ +import { json } from "@codemirror/lang-json"; +import { EditorState } from "@codemirror/state"; +import { EditorView, lineNumbers } from "@codemirror/view"; +import { codeHighlighting } from "./highlight.ts"; + +/** Read-only, focusable JSON view. */ +export const createJsonViewer = (parent: HTMLElement, label: string) => { + const view = new EditorView({ + parent, + state: EditorState.create({ + doc: "", + extensions: [ + EditorState.readOnly.of(true), + EditorView.editable.of(false), + lineNumbers(), + json(), + codeHighlighting, + EditorView.contentAttributes.of({ "aria-label": label, tabindex: "0" }), + ], + }), + }); + return { + show(text: string) { + view.dispatch({ changes: { from: 0, to: view.state.doc.length, insert: text } }); + view.scrollDOM.scrollTop = 0; + }, + text: () => view.state.doc.toString(), + }; +}; diff --git a/playground/tests/e2e/playground.spec.ts b/playground/tests/e2e/playground.spec.ts new file mode 100644 index 0000000..27f3344 --- /dev/null +++ b/playground/tests/e2e/playground.spec.ts @@ -0,0 +1,100 @@ +import { readFile } from "node:fs/promises"; +import { expect, test, type FrameLocator, type Page } from "@playwright/test"; + +const frame = (page: Page) => page.frameLocator('iframe[title="Python editor"]'); +const editor = (ws: FrameLocator) => ws.getByRole("textbox", { name: "Python code" }); +const tab = (ws: FrameLocator, name: string) => ws.getByRole("tab", { name, exact: true }); +const panel = (ws: FrameLocator) => ws.getByRole("tabpanel"); +const chip = (ws: FrameLocator) => ws.locator(".inspector-chip"); + +const replaceCode = async (page: Page, code: string) => { + await editor(frame(page)).click(); + await page.keyboard.press("ControlOrMeta+a"); + await page.keyboard.press("Delete"); + // insertText keeps indentation exactly; typing would trigger auto-indent. + await page.keyboard.insertText(code); +}; + +test.beforeEach(async ({ page }) => { + await page.goto("/"); + await page.evaluate(() => localStorage.clear()); + await page.reload(); +}); + +test("exports, verifies and visualizes the classifier example", async ({ page }) => { + const ws = frame(page); + await expect(chip(ws)).toHaveText("Verified"); + await expect(page).toHaveTitle("MLbundle Playground"); + await expect(page.getByRole("link", { name: "MLbundle home" })).toBeVisible(); + await expect(ws.locator(".inspector-title")).toContainText("classifier.mlbundle"); + await expect(panel(ws)).toContainText("DecisionTreeClassifier"); + await expect(panel(ws)).toContainText("Emscripten"); + // Narrow screens start with the code stacked above the inspector. + const narrow = page.viewportSize()!.width < 720; + await expect(ws.locator(".code-split")).toHaveAttribute("data-position", narrow ? "top" : "left"); + await expect(ws.getByRole("combobox", { name: "Bundle" })).toBeHidden(); + + await tab(ws, "Structure").click(); + await expect(panel(ws)).toContainText("Family: decision_tree"); + await expect(ws.getByRole("list", { name: "Decision tree" })).toContainText("≤"); + // Arrow keys move between tabs, as in the ARIA tabs pattern. + await tab(ws, "Structure").press("ArrowRight"); + await expect(tab(ws, "Members")).toBeFocused(); + await expect(panel(ws)).toContainText("model.skops"); + await tab(ws, "Members").press("ArrowRight"); + await expect(panel(ws)).toContainText("sample_rows=N"); + await tab(ws, "Samples").press("ArrowLeft"); + await tab(ws, "Members").press("Home"); + await expect(tab(ws, "Overview")).toHaveAttribute("aria-selected", "true"); + await tab(ws, "Schema").click(); + await expect(panel(ws)).toContainText("Predicted class"); + await tab(ws, "Manifest").click(); + await expect(ws.getByRole("textbox", { name: "Manifest JSON" })).toContainText('"format_name": "mlbundle"'); +}); + +test("downloads the written bundle and inspects uploads without loading them", async ({ page }) => { + const ws = frame(page); + await expect(chip(ws)).toHaveText("Verified"); + const [download] = await Promise.all([ + page.waitForEvent("download"), + ws.getByRole("button", { name: "Download .mlbundle" }).click(), + ]); + expect(download.suggestedFilename()).toBe("classifier.mlbundle"); + const bytes = await readFile((await download.path())!); + expect([...bytes.subarray(0, 2)]).toEqual([0x50, 0x4b]); + + const input = ws.locator('input[type="file"]'); + await input.setInputFiles({ name: "uploaded.mlbundle", mimeType: "application/zip", buffer: bytes }); + await expect(chip(ws)).toHaveText("Inspected only"); + await expect(ws.locator(".inspector-title")).toContainText("uploaded.mlbundle"); + await expect(ws.getByRole("button", { name: "Download .mlbundle" })).toBeHidden(); + await tab(ws, "Structure").click(); + await expect(panel(ws)).toContainText("without deserializing the model"); + + await input.setInputFiles({ name: "notes.mlbundle", mimeType: "application/zip", buffer: Buffer.from("hello") }); + await expect(chip(ws)).toHaveText("Rejected"); + await tab(ws, "Overview").click(); + await expect(panel(ws)).toContainText("InvalidContainerError"); +}); + +test("runs edited code, reports errors and keeps examples resettable", async ({ page }) => { + const ws = frame(page); + await expect(chip(ws)).toHaveText("Verified"); + await replaceCode(page, "from mlbundle import export\nprint('about to fail')\nexport(object(), 'x.mlbundle')"); + await page.keyboard.press("ControlOrMeta+Enter"); + await expect(tab(ws, "Console")).toHaveAttribute("aria-selected", "true"); + await expect(panel(ws)).toContainText("UnsupportedModelError"); + await expect(panel(ws)).toContainText("about to fail"); + + await ws.getByRole("combobox", { name: "Example" }).selectOption("regressor"); + await expect(ws.locator(".inspector-title")).toContainText("regressor.mlbundle"); + await tab(ws, "Structure").click(); + await expect(ws.getByRole("combobox", { name: "Tree" })).toHaveValue("0"); + await ws.getByRole("combobox", { name: "Tree" }).selectOption("2"); + await expect(panel(ws)).toContainText("Feature importances"); + + await replaceCode(page, "1 + 1"); + await ws.getByRole("button", { name: "Reset example" }).click(); + await expect(editor(ws)).toContainText("RandomForestRegressor"); + await expect(chip(ws)).toHaveText("Verified"); +}); diff --git a/playground/tests/runtime/examples.test.ts b/playground/tests/runtime/examples.test.ts new file mode 100644 index 0000000..23b4045 --- /dev/null +++ b/playground/tests/runtime/examples.test.ts @@ -0,0 +1,151 @@ +import assert from "node:assert/strict"; +import { readFileSync, readdirSync } from "node:fs"; +import { join } from "node:path"; +import { before, test } from "node:test"; +import type { BundleReport, Json, RunReport } from "../../src/runtime/protocol.ts"; +import { projectVersion, type PythonSession } from "../../src/runtime/session.ts"; +import { bootNodeSession, examplesDir, readExample, repoRoot } from "./load.ts"; + +type Obj = { [key: string]: Json }; +let session: PythonSession; +before(async () => { + session = await bootNodeSession(); +}); + +const runExample = async (file: string): Promise => { + const report = await session.run(readExample(file)); + assert.notEqual(report.outcome.kind, "error", JSON.stringify(report.outcome)); + return report; +}; +const only = (report: RunReport): BundleReport => { + assert.equal(report.bundles.length, 1); + const bundle = report.bundles[0]!; + assert.equal(bundle.status, "verified", bundle.error ?? ""); + assert.equal(bundle.source, "generated"); + return bundle; +}; +const value = (report: RunReport): unknown => { + assert.equal(report.outcome.kind, "json", JSON.stringify(report.outcome)); + return JSON.parse((report.outcome as { text: string }).text); +}; +const model = (bundle: BundleReport) => bundle.manifest!.model as Obj; +const names = (bundle: BundleReport) => bundle.members!.map((member) => member.name); + +test("the runtime runs the repository's mlbundle on the pinned wheels", () => { + assert.equal(session.versions.python.split(".").slice(0, 2).join("."), "3.14"); + assert.equal(session.versions.mlbundle, projectVersion(readFileSync(join(repoRoot, "pyproject.toml"), "utf8"))); + assert.equal(session.versions.mlschema, "0.3.0"); + assert.equal(session.versions.skops, "0.16.0"); + assert.match(session.versions["scikit-learn"], /^1\.8\./); +}); + +test("the example catalog is complete", () => { + const files = readdirSync(examplesDir).filter((file) => file.endsWith(".py")); + assert.deepEqual(files.sort(), [ + "classifier.py", + "errors.py", + "pipeline.py", + "regressor.py", + "samples.py", + ]); +}); + +test("the classifier example writes a verified bundle with a tree", async () => { + const report = await runExample("classifier.py"); + const bundle = only(report); + assert.deepEqual(value(report), [["cold", "warm"]]); + assert.equal(model(bundle).estimator_class, "DecisionTreeClassifier"); + assert.equal(model(bundle).kind, "classifier"); + assert.deepEqual(names(bundle), ["manifest.json", "model.skops", "schema.json", "structure.json"]); + for (const member of bundle.members!.slice(1)) assert.match(member.sha256 ?? "", /^[0-9a-f]{64}$/); + assert.equal(bundle.structure!.family, "decision_tree"); + assert.equal(bundle.samples, null); + // The download is the archive mlbundle wrote: a ZIP of the reported size. + assert.equal(bundle.bytes!.length, bundle.sizeBytes); + assert.deepEqual([...bundle.bytes!.subarray(0, 4)], [0x50, 0x4b, 0x03, 0x04]); + const environment = bundle.manifest!.environment as Obj; + assert.equal(environment.os, "Emscripten"); + assert.equal((environment.packages as Obj)["scikit-learn"], session.versions["scikit-learn"]); +}); + +test("the regressor example records one tree per estimator", async () => { + const bundle = only(await runExample("regressor.py")); + assert.equal(model(bundle).kind, "regressor"); + assert.equal(bundle.structure!.family, "tree_ensemble"); + assert.equal((bundle.structure!.trees as Json[]).length, 3); + assert.deepEqual((bundle.manifest!.features as Obj).names, ["area_m2", "rooms", "floor"]); +}); + +test("samples appear only when requested and one-hot columns form one field", async () => { + const report = await runExample("samples.py"); + const bundle = only(report); + assert.deepEqual(value(report), ["number", "onehot-category", "boolean"]); + assert.equal(bundle.samples!.length, 3); + assert.ok(names(bundle).includes("samples.json")); + assert.equal(bundle.structure!.family, "linear"); +}); + +test("an all-sklearn pipeline is verified with null structure", async () => { + const bundle = only(await runExample("pipeline.py")); + assert.equal(bundle.structure, null); + assert.ok(!names(bundle).includes("structure.json")); + assert.equal((bundle.schema as Obj).source, "default"); +}); + +test("rejections happen at export and write nothing", async () => { + const report = await runExample("errors.py"); + assert.equal(report.bundles.length, 0); + const results = value(report) as Obj[]; + assert.deepEqual( + results.map((entry) => [entry.case, entry.error]), + [ + ["custom subclass", "UnsupportedModelError"], + ["HistGradientBoosting", "UnsupportedModelError"], + ["samples without a frame", "InvalidSamplesError"], + ], + ); +}); + +test("an uploaded bundle is inspected without loading its model", async () => { + const generated = only(await runExample("classifier.py")); + const report = await session.inspect("my model.mlbundle", generated.bytes!.slice()); + const bundle = report.bundles[0]!; + assert.equal(bundle.status, "inspected"); + assert.equal(bundle.source, "uploaded"); + assert.equal(bundle.fileName, "my model.mlbundle"); + assert.equal(bundle.structure, null); + assert.deepEqual(bundle.manifest, generated.manifest); + assert.deepEqual(bundle.schema, generated.schema); + assert.equal(bundle.bytes, undefined); +}); + +test("damaged and non-bundle uploads are rejected by mlbundle.inspect", async () => { + const generated = only(await runExample("classifier.py")); + const tampered = generated.bytes!.slice(); + const middle = Math.floor(tampered.length / 2); + tampered[middle] = tampered[middle]! ^ 0xff; + const damaged = (await session.inspect("damaged.mlbundle", tampered)).bundles[0]!; + assert.equal(damaged.status, "rejected"); + assert.match(damaged.error!, /Error: /); + const notes = new TextEncoder().encode("hello"); + const text = (await session.inspect("notes.mlbundle", notes)).bundles[0]!; + assert.equal(text.status, "rejected"); + assert.match(text.error!, /^InvalidContainerError/); +}); + +test("user edits run as written in a fresh namespace and directory", async () => { + const failed = await session.run("from mlbundle import export\nexport(object(), 'x.mlbundle')"); + assert.equal(failed.outcome.kind, "error"); + const traceback = (failed.outcome as { text: string }).text; + assert.match(traceback, /File "", line 2[\s\S]*UnsupportedModelError/); + assert.equal(failed.bundles.length, 0); + + const stray = await session.run("open('stray.mlbundle', 'wb').write(b'not a zip')\nleftover = 1"); + assert.equal(stray.bundles[0]!.status, "rejected"); + assert.equal(stray.bundles[0]!.bytes, undefined); + const next = await session.run("import os\nsorted(os.listdir('.'))"); + assert.deepEqual(next.outcome, { kind: "json", text: "[]" }); + assert.deepEqual(next.bundles, []); + const name = await session.run("leftover"); + assert.match((name.outcome as { text: string }).text, /NameError/); +}); diff --git a/playground/tests/runtime/load.ts b/playground/tests/runtime/load.ts new file mode 100644 index 0000000..a8cf690 --- /dev/null +++ b/playground/tests/runtime/load.ts @@ -0,0 +1,40 @@ +import { readFileSync, readdirSync } from "node:fs"; +import { join, relative } from "node:path"; +import { fileURLToPath } from "node:url"; +import { loadPyodide } from "pyodide"; +import { startSession, type PythonSession, type SourceTree } from "../../src/runtime/session.ts"; + +const here = fileURLToPath(new URL(".", import.meta.url)); +export const repoRoot = join(here, "../../.."); +export const examplesDir = join(here, "../../src/examples"); + +const packageFiles = (dir: string): string[] => + readdirSync(dir, { withFileTypes: true }).flatMap((entry) => { + const path = join(dir, entry.name); + if (entry.isDirectory()) return entry.name === "__pycache__" ? [] : packageFiles(path); + return /\.(py|json)$/.test(entry.name) ? [path] : []; + }); + +/** Same tree the Vite build bundles: every .py and .json file under src/mlbundle. */ +export const readSources = (): SourceTree => { + const srcRoot = join(repoRoot, "src"); + return Object.fromEntries( + packageFiles(join(srcRoot, "mlbundle")).map((path) => [ + relative(srcRoot, path).replaceAll("\\", "/"), + readFileSync(path, "utf8"), + ]), + ); +}; + +export const readExample = (file: string) => readFileSync(join(examplesDir, file), "utf8"); + +/** Boots real Pyodide with the pinned wheels; downloads from jsDelivr and PyPI. */ +export const bootNodeSession = async (): Promise => { + const pyodide = await loadPyodide({ stdout: () => {}, stderr: () => {} }); + return startSession( + pyodide, + readSources(), + readFileSync(join(repoRoot, "pyproject.toml"), "utf8"), + readFileSync(join(here, "../../src/runtime/harness.py"), "utf8"), + ); +}; diff --git a/playground/tests/unit/client.test.ts b/playground/tests/unit/client.test.ts new file mode 100644 index 0000000..9a67172 --- /dev/null +++ b/playground/tests/unit/client.test.ts @@ -0,0 +1,203 @@ +import assert from "node:assert/strict"; +import { test } from "node:test"; +import { + createPythonRuntime, + type RuntimeState, + type WorkerLike, +} from "../../src/runtime/client.ts"; +import type { FromWorker, RunReport, ToWorker } from "../../src/runtime/protocol.ts"; + +const VERSIONS = { + python: "3.14.2", + mlbundle: "0.2.0", + mlschema: "0.3.0", + "scikit-learn": "1.8.0", + skops: "0.16.0", + pandas: "3.0.2", + numpy: "2.4.6", +}; +const NONE: RunReport = { outcome: { kind: "none" }, bundles: [], skipped: 0 }; +const JSON_REPORT: RunReport = { outcome: { kind: "json", text: "[]" }, bundles: [], skipped: 0 }; +const LIMITS = { bootMs: 40, runMs: 40 }; + +class FakeWorker implements WorkerLike { + onmessage: WorkerLike["onmessage"] = null; + onerror: WorkerLike["onerror"] = null; + sent: ToWorker[] = []; + transfers: Transferable[][] = []; + terminated = false; + failPost = false; + postMessage(message: ToWorker, transfer: Transferable[] = []) { + if (this.failPost) throw new Error("could not clone message"); + this.sent.push(message); + this.transfers.push(transfer); + } + terminate() { + this.terminated = true; + } + emit(data: FromWorker) { + this.onmessage?.({ data } as MessageEvent); + } + crash(message: string) { + this.onerror?.({ message, preventDefault() {} } as ErrorEvent); + } +} + +const setup = (limits = LIMITS) => { + const workers: FakeWorker[] = []; + const states: RuntimeState[] = []; + const streams: string[] = []; + const runtime = createPythonRuntime( + () => { + const worker = new FakeWorker(); + workers.push(worker); + return worker; + }, + { onState: (state) => states.push(state), onStream: (_, text) => streams.push(text) }, + limits, + ); + const latest = () => workers.at(-1)!; + return { runtime, workers, states, streams, latest }; +}; + +const ready = () => { + const context = setup(); + context.latest().emit({ type: "ready", versions: VERSIONS }); + return context; +}; + +test("boots, reports progress and becomes ready", () => { + const { runtime, latest, states } = setup(); + latest().emit({ type: "progress", message: "Loading scikit-learn and pandas…" }); + latest().emit({ type: "ready", versions: VERSIONS }); + assert.deepEqual( + states.map((state) => state.status), + ["booting", "booting", "ready"], + ); + assert.deepEqual(runtime.state, { status: "ready", versions: VERSIONS }); + runtime.dispose(); +}); + +test("runs code, forwards its streams and ignores stale run ids", async () => { + const { runtime, latest, streams } = ready(); + const result = runtime.run("print(1)"); + assert.equal(runtime.state.status, "running"); + const { id } = latest().sent[0]!; + latest().emit({ type: "stream", id: id + 1, stream: "stdout", text: "stale\n" }); + latest().emit({ type: "stream", id, stream: "stdout", text: "1\n" }); + latest().emit({ type: "done", id: id + 1, report: NONE, durationMs: 1 }); + latest().emit({ type: "done", id, report: JSON_REPORT, durationMs: 3 }); + assert.deepEqual(await result, { kind: "finished", report: JSON_REPORT, durationMs: 3 }); + assert.deepEqual(latest().sent[0], { type: "run", id, code: "print(1)" }); + assert.deepEqual(streams, ["1\n"]); + assert.equal(runtime.state.status, "ready"); + runtime.dispose(); +}); + +test("inspects an upload by transferring its bytes and returns the report", async () => { + const { runtime, latest } = ready(); + const data = new Uint8Array([0x50, 0x4b, 3, 4]).buffer; + const result = runtime.inspect("model.mlbundle", data); + assert.equal(runtime.state.status, "running"); + const sent = latest().sent[0]!; + assert.equal(sent.type, "inspect"); + assert.equal(sent.type === "inspect" && sent.name, "model.mlbundle"); + assert.deepEqual(latest().transfers[0], [data], "the buffer is transferred, not copied"); + const report: RunReport = { + outcome: { kind: "none" }, + skipped: 0, + bundles: [ + { fileName: "model.mlbundle", sizeBytes: 4, source: "uploaded", status: "rejected", error: "InvalidContainerError: x" }, + ], + }; + latest().emit({ type: "done", id: sent.id, report, durationMs: 2 }); + assert.deepEqual(await result, { kind: "finished", report, durationMs: 2 }); + assert.equal(runtime.state.status, "ready"); + const blocked = runtime.inspect("again.mlbundle", new ArrayBuffer(1)); + assert.deepEqual(await runtime.run("1"), { kind: "unavailable" }, "one request at a time"); + latest().emit({ type: "done", id: latest().sent[1]!.id, report: NONE, durationMs: 1 }); + assert.equal((await blocked).kind, "finished"); + runtime.dispose(); +}); + +test("refuses to run before Python is ready", async () => { + const { runtime, latest } = setup(); + assert.deepEqual(await runtime.run("1"), { kind: "unavailable" }); + assert.equal(latest().sent.length, 0); + runtime.dispose(); +}); + +test("a run past the time limit terminates the worker and boots a new one", async () => { + const { runtime, workers } = ready(); + assert.deepEqual(await runtime.run("while True: pass"), { kind: "timeout", limitMs: 40 }); + assert.equal(workers[0]!.terminated, true); + assert.equal(workers.length, 2); + assert.equal(runtime.state.status, "booting"); + runtime.dispose(); +}); + +test("cancel stops the run by restarting Python", async () => { + const { runtime, workers } = ready(); + const result = runtime.run("while True: pass"); + runtime.cancel(); + assert.deepEqual(await result, { kind: "cancelled" }); + assert.equal(workers[0]!.terminated, true); + assert.equal(workers.length, 2); + runtime.cancel(); + assert.equal(workers.length, 2, "cancel without a run does nothing"); + runtime.dispose(); +}); + +test("a worker crash during a run settles it as failed at once", async () => { + const { runtime, workers, latest } = ready(); + const result = runtime.run("1"); + latest().crash("out of memory"); + const message = "Python stopped unexpectedly: out of memory"; + assert.deepEqual(await result, { kind: "failed", message }); + assert.deepEqual(runtime.state, { status: "failed", message }); + assert.equal(workers[0]!.terminated, true); + await new Promise((resolve) => setTimeout(resolve, LIMITS.runMs * 2)); + assert.equal(workers.length, 1, "no restart until the visitor asks"); + runtime.restart(); + assert.equal(workers.length, 2); + runtime.dispose(); +}); + +test("boot errors and boot timeouts leave the runtime failed", async () => { + const errored = setup(); + errored.latest().emit({ type: "boot-error", message: "network down" }); + assert.deepEqual(errored.runtime.state, { + status: "failed", + message: "Python could not start: network down", + }); + const slow = setup(); + await new Promise((resolve) => setTimeout(resolve, LIMITS.bootMs * 2)); + assert.deepEqual(slow.runtime.state, { + status: "failed", + message: "Python could not start: no response within 0.04 seconds.", + }); + assert.equal(slow.latest().terminated, true); +}); + +test("a worker that cannot be created or messaged fails explicitly", async () => { + const runtime = createPythonRuntime( + () => { + throw new Error("Worker is not supported"); + }, + { onState() {}, onStream() {} }, + LIMITS, + ); + assert.deepEqual(runtime.state, { + status: "failed", + message: "Python could not start: Worker is not supported", + }); + assert.deepEqual(await runtime.run("1"), { kind: "unavailable" }); + + const { runtime: posting, latest } = ready(); + latest().failPost = true; + assert.deepEqual(await posting.run("1"), { + kind: "failed", + message: "Python stopped unexpectedly: could not clone message", + }); + assert.equal(posting.state.status, "failed"); +}); diff --git a/playground/tests/unit/config.test.ts b/playground/tests/unit/config.test.ts new file mode 100644 index 0000000..18b1e5f --- /dev/null +++ b/playground/tests/unit/config.test.ts @@ -0,0 +1,64 @@ +import assert from "node:assert/strict"; +import { readFileSync } from "node:fs"; +import { test } from "node:test"; +import { projectVersion } from "../../src/runtime/session.ts"; +import { PYODIDE_VERSION, PYPI_PINS } from "../../src/runtime/versions.ts"; +import { readWorkspaceState, saveWorkspaceState } from "../../src/workspace/state.ts"; + +const FALLBACK = { code: "x", exampleId: "classifier", position: "left", ratio: 50 } as const; +const read = (path: string) => readFileSync(new URL(path, import.meta.url), "utf8"); + +test("the CDN loader version matches the installed pyodide package", () => { + const installed = JSON.parse(read("../../node_modules/pyodide/package.json")) as { version: string }; + assert.equal(PYODIDE_VERSION, installed.version); +}); + +test("pure-Python pins match the repository lock", () => { + const lock = read("../../../uv.lock"); + for (const pin of PYPI_PINS) { + const [name, version] = pin.split("==") as [string, string]; + assert.match(lock, new RegExp(`name = "${name}"\nversion = "${version.replaceAll(".", "\\.")}"`), pin); + } +}); + +test("the bundled package reports the version pyproject.toml declares", () => { + const pyproject = read("../../../pyproject.toml"); + const declared = /^version = "([^"]+)"/m.exec(pyproject)![1]; + assert.equal(projectVersion(pyproject), declared); + assert.equal(projectVersion('[tool.x]\nversion = "9"\n[project]\nname = "a"\nversion = "1.2"\n'), "1.2"); + assert.throws(() => projectVersion('[tool.x]\nversion = "9"\n'), /no \[project\] version/); +}); + +test("workspace state restores valid fields and falls back on invalid ones", () => { + const storage = new Map(); + const backend = { + getItem: (key: string) => storage.get(key) ?? null, + setItem: (key: string, value: string) => void storage.set(key, value), + }; + assert.deepEqual(readWorkspaceState(backend, FALLBACK), FALLBACK); + assert.equal( + saveWorkspaceState(backend, { code: "y", exampleId: "samples", position: "top", ratio: 95 }), + true, + ); + assert.deepEqual(readWorkspaceState(backend, FALLBACK), { + code: "y", + exampleId: "samples", + position: "top", + ratio: 80, + }); + assert.equal([...storage.keys()][0], "mlbundle-playground-workspace"); + storage.set([...storage.keys()][0]!, '{"position":"diagonal","ratio":"wide"}'); + assert.deepEqual(readWorkspaceState(backend, FALLBACK), FALLBACK); + storage.set([...storage.keys()][0]!, "{not json"); + assert.deepEqual(readWorkspaceState(backend, FALLBACK), FALLBACK); +}); + +test("saving reports unavailable storage", () => { + const full = { + setItem() { + throw new Error("QuotaExceededError"); + }, + }; + assert.equal(saveWorkspaceState(full, FALLBACK), false); + assert.equal(saveWorkspaceState(undefined, FALLBACK), false); +}); diff --git a/playground/tests/unit/settings.test.ts b/playground/tests/unit/settings.test.ts new file mode 100644 index 0000000..80135f0 --- /dev/null +++ b/playground/tests/unit/settings.test.ts @@ -0,0 +1,104 @@ +import assert from "node:assert/strict"; +import { test } from "node:test"; +import { + APPEARANCES, + DEFAULT_SETTINGS, + readSettings, + saveSettings, + SETTINGS_KEY, + STYLES, + THEMES, + VIEWPORTS, + viewportSize, + type Settings, +} from "../../src/settings.ts"; + +const memory = (initial?: string) => { + const items = new Map(initial === undefined ? [] : [[SETTINGS_KEY, initial]]); + return { + items, + getItem: (key: string) => items.get(key) ?? null, + setItem: (key: string, value: string) => void items.set(key, value), + }; +}; +const CHOSEN: Settings = { + theme: "cobalt", + style: "contrast", + appearance: "dark", + viewport: "custom", + width: 500, + height: 700, +}; + +test("catalogs offer MLForm's choices with unique ids", () => { + assert.deepEqual( + THEMES.map(({ id }) => id), + ["eshkra", "m3diseen", "dosetailor", "clickhouse", "airbnb", "neutral", "cobalt", "sage", "sunset", "graphite"], + ); + assert.deepEqual(STYLES.map(({ label }) => label), ["Default", "Minimal", "Soft", "Contrast"]); + assert.deepEqual(APPEARANCES.map(({ label }) => label), ["System", "Light", "Dark"]); + for (const items of [THEMES, STYLES, APPEARANCES, VIEWPORTS]) { + assert.equal(new Set(items.map(({ id }) => id)).size, items.length); + } +}); + +test("saved settings round-trip", () => { + const storage = memory(); + assert.deepEqual(readSettings(storage), DEFAULT_SETTINGS); + assert.equal(saveSettings(storage, CHOSEN), true); + assert.deepEqual(readSettings(storage), CHOSEN); +}); + +test("each invalid field falls back alone", () => { + const saved = { ...CHOSEN, theme: "unknown", style: 3, viewport: "watch", width: 0 }; + assert.deepEqual(readSettings(memory(JSON.stringify(saved))), { + ...CHOSEN, + theme: DEFAULT_SETTINGS.theme, + style: DEFAULT_SETTINGS.style, + viewport: DEFAULT_SETTINGS.viewport, + width: DEFAULT_SETTINGS.width, + }); + for (const size of [8193, 1.5, -1, "640", null]) { + assert.equal(readSettings(memory(JSON.stringify({ height: size }))).height, DEFAULT_SETTINGS.height); + } + assert.equal(readSettings(memory(JSON.stringify({ width: 8192 }))).width, 8192); + assert.equal(readSettings(memory(JSON.stringify({ appearance: "sepia" }))).appearance, "auto"); +}); + +test("unreadable storage yields the defaults", () => { + for (const raw of ["{not json", "[1, 2]", "42", "null"]) { + assert.deepEqual(readSettings(memory(raw)), DEFAULT_SETTINGS); + } + const blocked = { + getItem(): string { + throw new Error("SecurityError"); + }, + }; + assert.deepEqual(readSettings(blocked), DEFAULT_SETTINGS); + assert.deepEqual(readSettings(undefined), DEFAULT_SETTINGS); +}); + +test("saving reports unavailable storage", () => { + const full = { + setItem() { + throw new Error("QuotaExceededError"); + }, + }; + assert.equal(saveSettings(full, CHOSEN), false); + assert.equal(saveSettings(undefined, CHOSEN), false); +}); + +test("viewport size follows the preset or the custom size", () => { + assert.equal(viewportSize(DEFAULT_SETTINGS), null); + assert.deepEqual(viewportSize({ ...CHOSEN, viewport: "phone" }), { + width: 390, + height: 844, + name: "Phone", + }); + assert.deepEqual(viewportSize({ ...CHOSEN, viewport: "ultrawide" }), { + width: 2560, + height: 1080, + name: "Ultrawide", + }); + assert.deepEqual(viewportSize(CHOSEN), { width: 500, height: 700, name: "Custom" }); +}); diff --git a/playground/tsconfig.json b/playground/tsconfig.json new file mode 100644 index 0000000..0dc1a5f --- /dev/null +++ b/playground/tsconfig.json @@ -0,0 +1,19 @@ +{ + "compilerOptions": { + "target": "ES2022", + "module": "ESNext", + "moduleResolution": "Bundler", + "lib": ["ES2023", "DOM", "DOM.Iterable", "WebWorker.ImportScripts"], + "types": ["node", "vite/client"], + "strict": true, + "noUncheckedIndexedAccess": true, + "noImplicitOverride": true, + "exactOptionalPropertyTypes": true, + "verbatimModuleSyntax": true, + "allowImportingTsExtensions": true, + "isolatedModules": true, + "skipLibCheck": true, + "noEmit": true + }, + "include": ["src/**/*.ts", "tests/**/*.ts", "*.ts"] +} diff --git a/playground/vite.config.ts b/playground/vite.config.ts new file mode 100644 index 0000000..74e0eed --- /dev/null +++ b/playground/vite.config.ts @@ -0,0 +1,11 @@ +import { fileURLToPath } from "node:url"; +import { defineConfig } from "vite-plus"; + +const repoRoot = fileURLToPath(new URL("..", import.meta.url)); + +export default defineConfig({ + // The worker bundles ../src/mlbundle and ../pyproject.toml; the header uses ../brand. + server: { fs: { allow: [repoRoot] } }, + worker: { format: "es" }, + build: { target: "es2022" }, +}); diff --git a/playground/wrangler.jsonc b/playground/wrangler.jsonc new file mode 100644 index 0000000..e428612 --- /dev/null +++ b/playground/wrangler.jsonc @@ -0,0 +1,12 @@ +{ + "name": "mlbundle-playground", + "compatibility_date": "2026-10-19", + "workers_dev": false, + "preview_urls": false, + "assets": { + "directory": "./dist", + }, + "routes": [ + { "pattern": "playground.mlbundle.eshkra.dev", "custom_domain": true }, + ], +} diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml new file mode 100644 index 0000000..e2d91b2 --- /dev/null +++ b/pnpm-lock.yaml @@ -0,0 +1,6106 @@ +--- +lockfileVersion: '9.0' + +importers: + + .: + configDependencies: {} + packageManagerDependencies: + pnpm: + specifier: 12.14.0 + version: 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100644 index 0000000..03c7697 --- /dev/null +++ b/pnpm-workspace.yaml @@ -0,0 +1,17 @@ +packages: + - landing + - docs + - playground + +allowBuilds: + esbuild: true + sharp: true + workerd: true + +overrides: + vite: npm:@voidzero-dev/vite-plus-core@1.3.0 + +# The alias reports the Vite+ version; it bundles compatible Vite 8.3.4. +peerDependencyRules: + allowedVersions: + vite: '1.3.0' diff --git a/pyproject.toml b/pyproject.toml index 4655c57..881253c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "mlbundle" -version = "0.1.0a3" +version = "0.2.0" description = "Pickle-free, verifiable model bundles with a manifest, schema and structure." readme = "README.md" license = "MIT" @@ -20,7 +20,6 @@ keywords = [ "skops", ] classifiers = [ - "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "Natural Language :: English", @@ -46,12 +45,12 @@ xgboost = ["xgboost>=3.4.1", "scikit-learn>=1.2"] # Audited CBM schema and Python 3.14 wheels on Windows/Linux. catboost = ["catboost>=1.2.10"] onnx = ["onnx>=1.23.0", "onnxruntime>=1.30.0"] -# The reader validates the 0.2 inferred vocabulary; 0.2.1 requires Python <3.15. -schema = ["mlschema>=0.2.1,<0.3; python_version == '3.14'"] +# The reader validates the 0.3 vocabulary; schema inference requires Python 3.14. +schema = ["mlschema>=0.3.0,<0.4; python_version == '3.14'"] all = [ "scikit-learn>=1.6", "skops>=0.16.0", "xgboost>=3.4.1", "catboost>=1.2.10", "onnx>=1.23.0", "onnxruntime>=1.30.0", - "mlschema>=0.2.1,<0.3; python_version == '3.14'", + "mlschema>=0.3.0,<0.4; python_version == '3.14'", ] [project.urls] diff --git a/scripts/package_smoke.py b/scripts/package_smoke.py index 5578ac5..01c733c 100644 --- a/scripts/package_smoke.py +++ b/scripts/package_smoke.py @@ -52,7 +52,7 @@ def _base(path: Path) -> None: import mlbundle - assert mlbundle.__version__ == "0.1.0a3" + assert mlbundle.__version__ == "0.2.0" view = mlbundle.inspect(path) assert view.manifest.model.library == "sklearn" assert "cannot attest" in _console("--help") diff --git a/scripts/publish_check.py b/scripts/publish_check.py index e778ab2..68f5536 100644 --- a/scripts/publish_check.py +++ b/scripts/publish_check.py @@ -1,6 +1,8 @@ -"""Read-only preflight for the owner's manual PyPI workflow.""" +"""Check a main-branch version change before the automatic PyPI workflow.""" +import argparse import os +import subprocess import sys import tomllib from pathlib import Path @@ -8,10 +10,13 @@ from urllib.request import urlopen -def check(ref: str, project: dict) -> None: +def check(ref: str, project: dict, previous_version: str | None = None) -> bool: if ref != "refs/heads/main": raise SystemExit("Publishing requires refs/heads/main; select main.") version = project["version"] + if version == previous_version: + sys.stdout.write(f"Version remains {version}; skipping PyPI publication.\n") + return False url = f"https://pypi.org/pypi/mlbundle/{version}/json" try: with urlopen(url, timeout=30): @@ -25,10 +30,36 @@ def check(ref: str, project: dict) -> None: else: raise SystemExit(f"mlbundle {version} already exists on PyPI; refusing upload.") sys.stdout.write(f"mlbundle {version} is not on PyPI (HTTP 404); main ref OK.\n") + return True + + +def version_at(ref: str) -> str | None: + if not ref or set(ref) == {"0"}: + return None + result = subprocess.run( + ["git", "show", f"{ref}:pyproject.toml"], + check=False, + capture_output=True, + text=True, + ) + if result.returncode: + raise SystemExit("Could not read the previous pyproject.toml version.") + return tomllib.loads(result.stdout)["project"]["version"] + + +def write_output(path: str, publish: bool) -> None: + with Path(path).open("a", encoding="utf-8") as output: + output.write(f"publish={str(publish).lower()}\n") if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--previous-ref") + args = parser.parse_args() project = tomllib.loads(Path("pyproject.toml").read_text(encoding="utf-8"))[ "project" ] - check(os.environ["GITHUB_REF"], project) + previous_version = version_at(args.previous_ref) if args.previous_ref else None + publish = check(os.environ["GITHUB_REF"], project, previous_version) + if args.previous_ref and os.environ.get("GITHUB_OUTPUT"): + write_output(os.environ["GITHUB_OUTPUT"], publish) diff --git a/src/mlbundle/_schema_inference.py b/src/mlbundle/_schema_inference.py deleted file mode 100644 index dd27903..0000000 --- a/src/mlbundle/_schema_inference.py +++ /dev/null @@ -1,28 +0,0 @@ -"""The temporary adaptation between local mlschema inference and MLForm.""" - - -def mlform_fields(fields: list[dict]) -> list[dict]: - # Adapt mlschema 0.2.1 to MLForm 0.1.25 until mlschema emits series columns. - return [ - { - **{ - key: value - for key, value in field.items() - if key not in ("field1", "field2") - }, - "columns": [ - { - **{ - key: value - for key, value in field[axis].items() - if key != "mappedTo" - }, - "id": axis, - } - for axis in ("field1", "field2") - ], - } - if field.get("kind") == "series" - else field - for field in fields - ] diff --git a/src/mlbundle/schema.py b/src/mlbundle/schema.py index fc3f616..6a5b9de 100644 --- a/src/mlbundle/schema.py +++ b/src/mlbundle/schema.py @@ -76,21 +76,6 @@ def reports_for(claims: ModelClaims) -> list[dict[str, JsonValue]]: return reports -def positional_fields(fields: list[dict], names: list[str]) -> None: - """Rewrite inferred name mappings, retaining mlschema's labels and grouping.""" - positions = {name: index for index, name in enumerate(names)} - - def rewrite(field: dict) -> None: - if "mappedTo" in field: - field["mappedTo"] = positions[field["mappedTo"]] - for option in field.get("options", []): - if isinstance(option, dict): - rewrite(option) - - for field in fields: - rewrite(field) - - def _mapping_index( mapping: str | int, positions: dict[str, int], origin: FeatureOrigin ) -> int: diff --git a/src/mlbundle/training.py b/src/mlbundle/training.py index 4909c9d..b927d87 100644 --- a/src/mlbundle/training.py +++ b/src/mlbundle/training.py @@ -9,7 +9,6 @@ from pydantic import ValidationError -from ._schema_inference import mlform_fields from .canonical import canonical_dumps, canonical_value from .claims import ModelClaims from .contract import FeatureOrigin, MemberName @@ -23,7 +22,7 @@ from .limits import ArchiveLimits from .manifest import FeatureInfo from .samples import sample_bytes -from .schema import positional_fields, reports_for, validate_schema, validate_separator +from .schema import reports_for, validate_schema, validate_separator if TYPE_CHECKING: from pandas import DataFrame @@ -125,9 +124,9 @@ def prepare_training( dtypes = [None] * claims.n_features cap = limits.member_bytes(MemberName.SCHEMA) try: - fields = mlform_fields(mlschema.infer_schema(frame, onehot_separator=separator)) + fields = mlschema.infer_schema(frame, onehot_separator=separator) if origin in (FeatureOrigin.DATAFRAME, FeatureOrigin.DECLARED): - positional_fields(fields, names) + fields = mlschema.with_positional_mappings(fields, names) schema = canonical_value( { "schema_version": "2", diff --git a/tests/test_publish.py b/tests/test_publish.py index b8f4bae..d195811 100644 --- a/tests/test_publish.py +++ b/tests/test_publish.py @@ -1,4 +1,4 @@ -"""Manual publish preflight rejects other refs, existing versions and outages.""" +"""Publish preflight gates version changes, duplicate releases and outages.""" import runpy from contextlib import nullcontext @@ -36,7 +36,7 @@ def response(url, timeout): monkeypatch.setitem(preflight.__globals__, "urlopen", response) if code == 404: - preflight("refs/heads/main", {"version": "0.1.0a3"}) + assert preflight("refs/heads/main", {"version": "0.1.0a3"}) is True assert "HTTP 404" in capsys.readouterr().out else: message = "already exists" if code == 200 else f"HTTP {code}" @@ -44,6 +44,26 @@ def response(url, timeout): preflight("refs/heads/main", {"version": "0.1.0a3"}) +def test_unchanged_version_skips_network(preflight, monkeypatch, capsys): + def unexpected(*args, **kwargs): + raise AssertionError("An unchanged version must not query PyPI") + + monkeypatch.setitem(preflight.__globals__, "urlopen", unexpected) + assert ( + preflight("refs/heads/main", {"version": "0.2.0"}, previous_version="0.2.0") + is False + ) + assert "skipping" in capsys.readouterr().out + + +def test_output_records_publish_decision(preflight, tmp_path): + write_output = preflight.__globals__["write_output"] + output = tmp_path / "github-output" + write_output(str(output), True) + write_output(str(output), False) + assert output.read_text(encoding="utf-8") == "publish=true\npublish=false\n" + + @pytest.mark.parametrize("error", [URLError("offline"), TimeoutError("timeout")]) def test_network_failure_is_closed(preflight, monkeypatch, error): def response(*args, **kwargs): diff --git a/tests/test_readme.py b/tests/test_readme.py index c7cf74b..1f38482 100644 --- a/tests/test_readme.py +++ b/tests/test_readme.py @@ -1,4 +1,4 @@ -"""Execute the actual fenced README examples, including the console commands.""" +"""Execute the README quick start and its mlbundle CLI commands.""" import re import shlex @@ -8,8 +8,6 @@ import pytest -from onnx_helpers import softmax - README = Path(__file__).resolve().parents[1] / "README.md" BLOCKS = re.findall( r"```(\w+)\n(.*?)\n```", README.read_text(encoding="utf-8"), re.DOTALL @@ -17,32 +15,20 @@ PYTHON = [code for language, code in BLOCKS if language == "python"] -@pytest.mark.parametrize( - "code", - PYTHON, - ids=[ - "sklearn", - "records", - "xgboost", - "catboost", - "composite", - "onnx", - "schema-samples", - ], -) +@pytest.mark.parametrize("code", PYTHON) def test_python_example(code, tmp_path, monkeypatch): monkeypatch.chdir(tmp_path) - if 'record = {"0"' in code: - exec(compile(PYTHON[0], str(README), "exec"), {}) - if 'Path("trained.onnx")' in code: - # The user's prerequisite file: a self-contained, two-column classifier. - Path("trained.onnx").write_bytes(softmax().SerializeToString()) - exec(compile(code, str(README), "exec"), {}) + scope = {} + exec(compile(code, str(README), "exec"), scope) + assert scope["metadata"].samples is None + assert scope["bundle"].feature_names == ("temperature", "humidity") + assert scope["result"].predicted_label == "cold" + assert scope["result"].probabilities == (1.0, 0.0) def test_console_examples(tmp_path, monkeypatch): - assert len(PYTHON) == 7 - assert all(language in {"python", "shell"} for language, _ in BLOCKS) + assert PYTHON + assert all(language in {"python", "shell", "bash"} for language, _ in BLOCKS) monkeypatch.chdir(tmp_path) exec(compile(PYTHON[0], str(README), "exec"), {}) executable = Path(sys.executable).with_name( diff --git a/tests/test_series_schema.py b/tests/test_series_schema.py index 3145c77..acdbcc4 100644 --- a/tests/test_series_schema.py +++ b/tests/test_series_schema.py @@ -1,4 +1,4 @@ -"""The sole mlschema shape adaptation and its closed reader contract.""" +"""Native mlschema series output and its closed reader contract.""" import copy from typing import get_args @@ -15,7 +15,7 @@ from mlbundle.writing import write_container -def test_field_vocabulary_matches_mlschema_except_series(): +def test_field_vocabulary_matches_mlschema(): from mlschema.strategies.app import builtin_kinds from mlschema.strategies.domain.onehot_category_field import OneHotCategoryOption @@ -27,12 +27,7 @@ def test_field_vocabulary_matches_mlschema_except_series(): theirs = {item.name: set(item.model.model_fields) for item in builtin_kinds()} assert ours.keys() == theirs.keys() for kind, keys in ours.items(): - if kind == "series": - # The MLForm columns adaptation is the only inferred shape difference. - assert keys - theirs[kind] == {"columns"} - assert {"field1", "field2"} <= theirs[kind] - keys - else: - assert keys <= theirs[kind], kind + assert keys <= theirs[kind], kind assert set(OneHotOption.model_fields) <= set(OneHotCategoryOption.model_fields) @@ -45,25 +40,7 @@ def series_bundle(tmp_path): ) original = infer_schema(frame)[0] field = view.schema["fields"][0] - assert field == { - **{ - key: value - for key, value in original.items() - if key not in ("field1", "field2", "mappedTo") - }, - "mappedTo": 0, - "columns": [ - { - **{ - key: value - for key, value in original[axis].items() - if key != "mappedTo" - }, - "id": axis, - } - for axis in ("field1", "field2") - ], - } + assert field == {**original, "mappedTo": 0} assert view.schema["schema_version"] == "2" assert mlbundle.inspect(path).schema == mlbundle.load(path).schema == view.schema return path, field @@ -76,15 +53,9 @@ def test_series_export(series_bundle): ] -def old_shape(field): - for column in field.pop("columns"): - field[column.pop("id")] = {**column, "mappedTo": field["mappedTo"]} - - @pytest.mark.parametrize( "change", [ - old_shape, lambda f: f["columns"].reverse(), lambda f: f["columns"].pop(), lambda f: f["columns"].append(copy.deepcopy(f["columns"][0])), diff --git a/uv.lock b/uv.lock index c777b7d..2e6358f 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 5 requires-python = ">=3.14" resolution-markers = [ "sys_platform == 'win32'", @@ -459,7 +459,7 @@ wheels = [ [[package]] name = "mlbundle" -version = "0.1.0a3" +version = "0.2.0" source = { editable = "." } dependencies = [ { name = "pydantic" }, @@ -509,8 +509,8 @@ dev = [ requires-dist = [ { name = "catboost", marker = "extra == 'all'", specifier = ">=1.2.10" }, { name = "catboost", marker = "extra == 'catboost'", specifier = ">=1.2.10" }, - { name = "mlschema", marker = "python_full_version == '3.14.*' and extra == 'all'", specifier = ">=0.2.1,<0.3" }, - { name = "mlschema", marker = "python_full_version == '3.14.*' and extra == 'schema'", specifier = ">=0.2.1,<0.3" }, + { name = "mlschema", marker = "python_full_version == '3.14.*' and extra == 'all'", specifier = ">=0.3.0,<0.4" }, + { name = "mlschema", marker = "python_full_version == '3.14.*' and extra == 'schema'", specifier = ">=0.3.0,<0.4" }, { name = "onnx", marker = "extra == 'all'", specifier = ">=1.23.0" }, { name = "onnx", marker = "extra == 'onnx'", specifier = ">=1.23.0" }, { name = "onnxruntime", marker = "extra == 'all'", specifier = ">=1.30.0" }, @@ -539,15 +539,15 @@ dev = [ [[package]] name = "mlschema" -version = "0.2.1" +version = "0.3.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "pandas" }, { name = "pydantic" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/a2/97/b3295985c4250525069b86bf0eee31c211b3d35cebee5b995f64a35dcb58/mlschema-0.2.1.tar.gz", hash = "sha256:59f819d677fa662f9c620938a6bef7fd093a11dad5872e507e5035da723cdb9e", size = 56763, upload-time = "2026-06-18T11:21:12.225Z" } +sdist = { url = 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Date: Mon, 19 Oct 2026 15:13:07 +0200 Subject: [PATCH 2/3] fix: use absolute README links for PyPI PyPI renders the README without the repository, so relative links to docs, LICENSE and site folders broke. Point them at the deployed sites and GitHub, and test that every README link is absolute. --- README.md | 37 +++++++++++++++++++------------------ tests/test_readme.py | 13 ++++++++++++- 2 files changed, 31 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 05065a7..a16d0a1 100644 --- a/README.md +++ b/README.md @@ -12,13 +12,13 @@

Your trained model, schema and verified structure. One file.

- Website source - Documentation - Playground + Website + Documentation + Playground Version 0.2.0 Source on GitHub CI status - MIT license + MIT license Part of eshkra

@@ -142,7 +142,7 @@ or training history. Custom types fail at export. Sklearn and CatBoost own their class labels; XGBoost can bind declared labels to a native copy. ONNX has no visualization structure, and all-sklearn Pipeline structure is null. The -[adapter contracts](./docs/format-adapters.md) define the exact support limits. +[adapter contracts](https://docs.mlbundle.eshkra.dev/format/adapters/) define the exact support limits. ## Some notes @@ -153,7 +153,7 @@ independent review; do not accept bundles from untrusted parties. The reader bounds ZIP/JSON data, verifies integrity and uses closed native loaders. Hashes detect corruption, not deliberate tampering. Native parsers and inference still need process isolation and application-owned resource limits. -Read [SECURITY.md](./SECURITY.md) for guarantees, limits and private reporting. +Read [SECURITY.md](https://docs.mlbundle.eshkra.dev/security/) for guarantees, limits and private reporting. Training rows stay local by default, but feature names, categories, labels and learned structure can reveal information. Schemas and environment records remain @@ -162,17 +162,18 @@ the library's scope. ## Documentation -The [documentation site](./docs/) includes installation, adapter guides, record -prediction and API reference. The normative contracts remain in this repository: +The [documentation site](https://docs.mlbundle.eshkra.dev) includes installation, adapter guides, record +prediction and API reference. It renders the normative contracts from this +repository unchanged: -- [Bundle format and verification](./docs/format.md) -- [Schema, mappings and explicit samples](./docs/format-schema.md) -- [Supported adapters and structure](./docs/format-adapters.md) -- [Sklearn state and trusted types](./docs/format-state.md) -- [Composite pipelines](./docs/format-pipeline.md) -- [ONNX layouts and bounds](./docs/format-onnx.md) +- [Bundle format and verification](https://docs.mlbundle.eshkra.dev/format/) +- [Schema, mappings and explicit samples](https://docs.mlbundle.eshkra.dev/format/schema/) +- [Supported adapters and structure](https://docs.mlbundle.eshkra.dev/format/adapters/) +- [Sklearn state and trusted types](https://docs.mlbundle.eshkra.dev/format/state/) +- [Composite pipelines](https://docs.mlbundle.eshkra.dev/format/pipeline/) +- [ONNX layouts and bounds](https://docs.mlbundle.eshkra.dev/format/onnx/) -The [playground](./playground/) provides editable examples and inspection of the +The [playground](https://playground.mlbundle.eshkra.dev) provides editable examples and inspection of the generated `.mlbundle` file. It runs scikit-learn examples in the browser and inspects uploaded files without loading their models. @@ -192,8 +193,8 @@ uv build ``` Tests use the installed package and enforce 80% branch-inclusive coverage. -CI validates the built wheel on Windows and Linux. Read [AGENTS.md](./AGENTS.md) -for contribution rules and [publishing](./docs/publishing.md) for the owner-approved +CI validates the built wheel on Windows and Linux. Read [AGENTS.md](https://github.com/UlloaSP/mlbundle/blob/main/AGENTS.md) +for contribution rules and [publishing](https://github.com/UlloaSP/mlbundle/blob/main/docs/publishing.md) for the automatic release workflow. The landing, docs and playground use [Vite+](https://viteplus.dev/guide/) and a @@ -218,7 +219,7 @@ After all checks pass on `main`, it deploys the three tested site builds to Cloudflare. Manual **CI Pipeline** runs on `main` use the same gates and can redeploy without publishing Python. A change to `project.version` on `main` automatically starts the PyPI workflow after its version and branch preflight. -See [website deployment](./docs/deployment.md) and [publishing](./docs/publishing.md). +See [website deployment](https://github.com/UlloaSP/mlbundle/blob/main/docs/deployment.md) and [publishing](https://github.com/UlloaSP/mlbundle/blob/main/docs/publishing.md). ## License diff --git a/tests/test_readme.py b/tests/test_readme.py index 1f38482..011f512 100644 --- a/tests/test_readme.py +++ b/tests/test_readme.py @@ -1,4 +1,4 @@ -"""Execute the README quick start and its mlbundle CLI commands.""" +"""Execute the README quick start and check the links PyPI renders.""" import re import shlex @@ -13,6 +13,17 @@ r"```(\w+)\n(.*?)\n```", README.read_text(encoding="utf-8"), re.DOTALL ) PYTHON = [code for language, code in BLOCKS if language == "python"] +LINKS = re.findall( + r"\]\(([^)\s]+)\)|(?:href|src|srcset)=\"([^\"]+)\"", + README.read_text(encoding="utf-8"), +) + + +def test_links_are_absolute(): + # PyPI renders the README without the repository, so relative links break. + targets = [markdown or html for markdown, html in LINKS] + assert targets + assert [url for url in targets if not url.startswith("https://")] == [] @pytest.mark.parametrize("code", PYTHON) From ccf2ab12d7372d99a3d87a54645c083fb5542329 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pablo=20Ulloa=20Sant=C3=ADn?= Date: Mon, 19 Oct 2026 15:30:24 +0200 Subject: [PATCH 3/3] chore: release mlbundle 0.2.1 Publish the absolute README links to PyPI; 0.2.0's project page keeps the broken relative links because published releases cannot change. --- AGENTS.md | 2 +- README.md | 6 +++--- SECURITY.md | 2 +- docs/publishing.md | 4 ++-- .../docs/getting-started/installation.md | 10 +++++----- docs/src/content/docs/guides/cli.md | 4 ++-- .../content/docs/guides/inspect-and-load.md | 2 +- landing/src/data/example.json | 20 +++++++++---------- playground/README.md | 2 +- pyproject.toml | 2 +- scripts/package_smoke.py | 2 +- uv.lock | 2 +- 12 files changed, 29 insertions(+), 29 deletions(-) diff --git a/AGENTS.md b/AGENTS.md index adee395..8b59b4a 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -5,7 +5,7 @@ inferred schema and model structure in one compressed `.mlbundle` file. It owns export, metadata-only inspection and validated loading. It is the training-side companion to MLSuite, whose runtime accepts `.mlbundle` uploads. -MLSuite does not train models. The package version is 0.2.0; format 1 is not frozen. +MLSuite does not train models. The package version is 0.2.1; format 1 is not frozen. Implementation claims require code and test proof. ## What must remain true diff --git a/README.md b/README.md index a16d0a1..7c19263 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ Website Documentation Playground - Version 0.2.0 + Version 0.2.1 Source on GitHub CI status MIT license @@ -43,11 +43,11 @@ preserve the supported model without conversion. ## Installation -MLbundle **0.2.0** requires Python **3.14** for export with the schema extra. +MLbundle **0.2.1** requires Python **3.14** for export with the schema extra. The installation command for this release is: ```bash -python -m pip install "mlbundle[sklearn,schema]==0.2.0" +python -m pip install "mlbundle[sklearn,schema]==0.2.1" ``` To use a local checkout, run `python -m pip install -e ".[sklearn,schema]"` diff --git a/SECURITY.md b/SECURITY.md index 5f1ed08..f52044a 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -1,6 +1,6 @@ # Security policy -MLbundle 0.2.0 has not had an independent security review; +MLbundle 0.2.1 has not had an independent security review; do not accept bundles from untrusted parties. Format version 1 is not frozen. It implements strict manifest validation, secure container reading/writing, metadata-only `inspect`, export/load, sklearn, XGBoost, CatBoost and ONNX adapters, local schema inference, diff --git a/docs/publishing.md b/docs/publishing.md index ef847aa..5c8ad6c 100644 --- a/docs/publishing.md +++ b/docs/publishing.md @@ -1,6 +1,6 @@ # Publishing to PyPI -Version `0.2.0` is prepared by this checkout. Merging a version change into +Version `0.2.1` is prepared by this checkout. Merging a version change into `main` automatically starts the PyPI workflow; no one needs to start a release run manually. Format version 1 is not frozen, so incompatible manifest/schema shapes require re-export. Valid a2 bundles remain compatible, as documented in @@ -19,7 +19,7 @@ independent review; do not accept bundles from untrusted parties. tests on Ubuntu and Windows, and all frontend checks, tests and builds. 4. If every check passes, Actions uploads the exact CI-built wheel and source archive to [mlbundle on PyPI](https://pypi.org/project/mlbundle/). - Install with `python -m pip install "mlbundle[sklearn,schema]==0.2.0"` on + Install with `python -m pip install "mlbundle[sklearn,schema]==0.2.1"` on Python 3.14, replacing the example version as needed. The GitHub `pypi_env` environment contains the project-scoped diff --git a/docs/src/content/docs/getting-started/installation.md b/docs/src/content/docs/getting-started/installation.md index 17bd87e..854a464 100644 --- a/docs/src/content/docs/getting-started/installation.md +++ b/docs/src/content/docs/getting-started/installation.md @@ -1,15 +1,15 @@ --- title: Installation -description: Install mlbundle 0.2.0 with the extras for your model library, from PyPI or a local checkout, and check the Python and dependency ranges. +description: Install mlbundle 0.2.1 with the extras for your model library, from PyPI or a local checkout, and check the Python and dependency ranges. --- mlbundle requires **Python 3.14 or newer**. Its only core dependency is Pydantic v2; every model library is an optional extra that is imported lazily, only when a bundle needs it. -These pages document mlbundle 0.2.0. +These pages document mlbundle 0.2.1. ```bash -python -m pip install "mlbundle[sklearn,schema]==0.2.0" +python -m pip install "mlbundle[sklearn,schema]==0.2.1" ``` Replace `sklearn` with the extra for your model library; the extras are listed @@ -17,7 +17,7 @@ Replace `sklearn` with the extra for your model library; the extras are listed ## From a local checkout -To work from source, or before 0.2.0 is available from your package index, install a checkout of +To work from source, or before 0.2.1 is available from your package index, install a checkout of the repository: ```bash @@ -66,7 +66,7 @@ from importlib.metadata import version import mlbundle -print(version("mlbundle")) # 0.2.0 +print(version("mlbundle")) # 0.2.1 print(mlbundle.FORMAT_NAME, mlbundle.FORMAT_VERSION) # mlbundle 1 ``` diff --git a/docs/src/content/docs/guides/cli.md b/docs/src/content/docs/guides/cli.md index 14e94c3..89d7bbe 100644 --- a/docs/src/content/docs/guides/cli.md +++ b/docs/src/content/docs/guides/cli.md @@ -22,7 +22,7 @@ Metadata and integrity validated; model claims NOT verified. Format: mlbundle 1 Bundle id: aa6f6b39-574a-499b-a0ff-de4cdd334050 Created: 2026-10-19T11:17:51.949658+00:00 -Writer: "0.2.0" +Writer: "0.2.1" Model: sklearn "DecisionTreeClassifier" (classifier) Counts: features=2, outputs=1, classes=[2] Features (declared): ["temperature", "humidity"] @@ -34,7 +34,7 @@ Members: schema.json: 361 bytes sha256=ea62a6a7502ae8ea30b45b61bb18d8e31bd2ec55f026d9fbf0c05ef42d01ff25 structure.json: 526 bytes sha256=66204da26781a4e2a800a1f3ed246ab1a43e8c97ced73d06a2640f897eeda03c Training: {"python": "3.14.7", "implementation": "CPython", "os": "Windows", "os_version": "11", "architecture": "AMD64"} -Packages: {"catboost": "1.2.10", "mlbundle": "0.2.0", "mlschema": "0.3.0", ...} +Packages: {"catboost": "1.2.10", "mlbundle": "0.2.1", "mlschema": "0.3.0", ...} ONNX environment: null Present: schema=yes, structure=yes, samples=no User metadata: {} diff --git a/docs/src/content/docs/guides/inspect-and-load.md b/docs/src/content/docs/guides/inspect-and-load.md index 69ea76c..0d7d53c 100644 --- a/docs/src/content/docs/guides/inspect-and-load.md +++ b/docs/src/content/docs/guides/inspect-and-load.md @@ -27,7 +27,7 @@ from mlbundle import inspect view = inspect("trained.mlbundle") manifest = view.manifest -print(manifest.format_version, manifest.writer_version) # 1 0.2.0 +print(manifest.format_version, manifest.writer_version) # 1 0.2.1 print(manifest.model.n_features, manifest.model.n_outputs) # 2 1 print([member.value for member in view.members]) # ['manifest.json', 'model.skops', 'schema.json', 'structure.json'] diff --git a/landing/src/data/example.json b/landing/src/data/example.json index acc01e0..6c0bc59 100644 --- a/landing/src/data/example.json +++ b/landing/src/data/example.json @@ -1,8 +1,8 @@ { - "version": "0.2.0", + "version": "0.2.1", "code": "import pandas as pd\nfrom sklearn.tree import DecisionTreeClassifier\n\nfrom mlbundle import export, load\n\ntraining = pd.DataFrame(\n [[0.0, 1.0, \"cold\"], [1.0, 0.0, \"warm\"]] * 2,\n columns=[\"temperature\", \"humidity\", \"target\"],\n)\nfeatures = training.drop(columns=\"target\")\nmodel = DecisionTreeClassifier(random_state=7)\nmodel.fit(features, training[\"target\"])\n\nexport(\n model,\n \"trained.mlbundle\",\n training_data=training,\n target_names=[\"target\"],\n)\n\nbundle = load(\"trained.mlbundle\")\nrecord = {\"temperature\": 0.0, \"humidity\": 1.0}\nprediction = bundle.predict_record(record)[0]\nprint(prediction.predicted_label, prediction.probabilities)\n", "printed": "cold (1.0, 0.0)", - "bundleBytes": 9227, + "bundleBytes": 9233, "members": [ { "name": "manifest.json", @@ -12,7 +12,7 @@ { "name": "model.skops", "size": 15011, - "sha256": "13257b547ae5a12c2bc5cea20afaf68d1a36d6073c8f77f1ae676979c40375d6" + "sha256": "b332c1e060939f16a2f300797c6ee1625e99d684f215e45ba8772fd7dfee5da2" }, { "name": "schema.json", @@ -28,9 +28,9 @@ "manifest": { "format_name": "mlbundle", "format_version": "1", - "bundle_id": "a9e717a9-b071-4d23-bbd1-6deedc47e6b5", - "created_at": "2026-10-19T11:17:33.114088Z", - "writer_version": "0.2.0", + "bundle_id": "869eb397-2dce-44b4-b0d0-b6a85d757b0b", + "created_at": "2026-10-19T13:27:32.304933Z", + "writer_version": "0.2.1", "model": { "estimator_class": "DecisionTreeClassifier", "kind": "classifier", @@ -90,7 +90,7 @@ "architecture": "AMD64", "packages": { "catboost": "1.2.10", - "mlbundle": "0.2.0", + "mlbundle": "0.2.1", "mlschema": "0.3.0", "numpy": "2.5.4", "onnx": "1.23.0", @@ -105,7 +105,7 @@ }, "integrity": { "model.skops": { - "sha256": "13257b547ae5a12c2bc5cea20afaf68d1a36d6073c8f77f1ae676979c40375d6", + "sha256": "b332c1e060939f16a2f300797c6ee1625e99d684f215e45ba8772fd7dfee5da2", "size": 15011 }, "schema.json": { @@ -159,7 +159,7 @@ }, "inspect": { "exit": 0, - "stdout": "Metadata and integrity validated; model claims NOT verified.\nFormat: mlbundle 1\nBundle id: a9e717a9-b071-4d23-bbd1-6deedc47e6b5\nCreated: 2026-10-19T11:17:33.114088+00:00\nWriter: \"0.2.0\"\nModel: sklearn \"DecisionTreeClassifier\" (classifier)\nCounts: features=2, outputs=1, classes=[2]\nFeatures (model): [\"temperature\", \"humidity\"]\nClass labels (output-major): [[\"cold\", \"warm\"]]\nTarget names (output-major): [\"target\"]\nMembers:\n manifest.json: validated (no self-integrity record)\n model.skops: 15011 bytes sha256=13257b547ae5a12c2bc5cea20afaf68d1a36d6073c8f77f1ae676979c40375d6\n schema.json: 388 bytes sha256=e770ad53af881faf368c686de86b2b00f0a64a1444e241985643349bf7b0bba4\n structure.json: 511 bytes sha256=8cf658be94b826ba4f81f1a78ed2c8e5107fce656d212dc888bd8621a9068baa\nTraining: {\"python\": \"3.14.7\", \"implementation\": \"CPython\", \"os\": \"Windows\", \"os_version\": \"11\", \"architecture\": \"AMD64\"}\nPackages: {\"catboost\": \"1.2.10\", \"mlbundle\": \"0.2.0\", \"mlschema\": \"0.3.0\", \"numpy\": \"2.5.4\", \"onnx\": \"1.23.0\", \"onnxruntime\": \"1.30.0\", \"pandas\": \"3.0.6\", \"scikit-learn\": \"1.9.1\", \"scipy\": \"1.18.1\", \"skops\": \"0.16.0\", \"xgboost\": \"3.4.1\"}\nONNX environment: null\nPresent: schema=yes, structure=yes, samples=no\nUser metadata: {}", + "stdout": "Metadata and integrity validated; model claims NOT verified.\nFormat: mlbundle 1\nBundle id: 869eb397-2dce-44b4-b0d0-b6a85d757b0b\nCreated: 2026-10-19T13:27:32.304933+00:00\nWriter: \"0.2.1\"\nModel: sklearn \"DecisionTreeClassifier\" (classifier)\nCounts: features=2, outputs=1, classes=[2]\nFeatures (model): [\"temperature\", \"humidity\"]\nClass labels (output-major): [[\"cold\", \"warm\"]]\nTarget names (output-major): [\"target\"]\nMembers:\n manifest.json: validated (no self-integrity record)\n model.skops: 15011 bytes sha256=b332c1e060939f16a2f300797c6ee1625e99d684f215e45ba8772fd7dfee5da2\n schema.json: 388 bytes sha256=e770ad53af881faf368c686de86b2b00f0a64a1444e241985643349bf7b0bba4\n structure.json: 511 bytes sha256=8cf658be94b826ba4f81f1a78ed2c8e5107fce656d212dc888bd8621a9068baa\nTraining: {\"python\": \"3.14.7\", \"implementation\": \"CPython\", \"os\": \"Windows\", \"os_version\": \"11\", \"architecture\": \"AMD64\"}\nPackages: {\"catboost\": \"1.2.10\", \"mlbundle\": \"0.2.1\", \"mlschema\": \"0.3.0\", \"numpy\": \"2.5.4\", \"onnx\": \"1.23.0\", \"onnxruntime\": \"1.30.0\", \"pandas\": \"3.0.6\", \"scikit-learn\": \"1.9.1\", \"scipy\": \"1.18.1\", \"skops\": \"0.16.0\", \"xgboost\": \"3.4.1\"}\nONNX environment: null\nPresent: schema=yes, structure=yes, samples=no\nUser metadata: {}", "stderr": "" }, "verify": { @@ -169,7 +169,7 @@ }, "editedInspect": { "exit": 0, - "stdout": "Metadata and integrity validated; model claims NOT verified.\nFormat: mlbundle 1\nBundle id: a9e717a9-b071-4d23-bbd1-6deedc47e6b5\nCreated: 2026-10-19T11:17:33.114088+00:00\nWriter: \"0.2.0\"\nModel: sklearn \"DecisionTreeClassifier\" (classifier)\nCounts: features=2, outputs=1, classes=[2]\nFeatures (model): [\"temperature\", \"humidity\"]\nClass labels (output-major): [[\"cold\", \"warm\"]]\nTarget names (output-major): [\"target\"]\nMembers:\n manifest.json: validated (no self-integrity record)\n model.skops: 15011 bytes sha256=13257b547ae5a12c2bc5cea20afaf68d1a36d6073c8f77f1ae676979c40375d6\n schema.json: 388 bytes sha256=e770ad53af881faf368c686de86b2b00f0a64a1444e241985643349bf7b0bba4\n structure.json: 511 bytes sha256=8cf658be94b826ba4f81f1a78ed2c8e5107fce656d212dc888bd8621a9068baa\nTraining: {\"python\": \"3.14.7\", \"implementation\": \"CPython\", \"os\": \"Windows\", \"os_version\": \"11\", \"architecture\": \"AMD64\"}\nPackages: {\"catboost\": \"1.2.10\", \"mlbundle\": \"0.2.0\", \"mlschema\": \"0.3.0\", \"numpy\": \"2.5.4\", \"onnx\": \"1.23.0\", \"onnxruntime\": \"1.30.0\", \"pandas\": \"3.0.6\", \"scikit-learn\": \"1.9.1\", \"scipy\": \"1.18.1\", \"skops\": \"0.16.0\", \"xgboost\": \"3.4.1\"}\nONNX environment: null\nPresent: schema=yes, structure=yes, samples=no\nUser metadata: {}", + "stdout": "Metadata and integrity validated; model claims NOT verified.\nFormat: mlbundle 1\nBundle id: 869eb397-2dce-44b4-b0d0-b6a85d757b0b\nCreated: 2026-10-19T13:27:32.304933+00:00\nWriter: \"0.2.1\"\nModel: sklearn \"DecisionTreeClassifier\" (classifier)\nCounts: features=2, outputs=1, classes=[2]\nFeatures (model): [\"temperature\", \"humidity\"]\nClass labels (output-major): [[\"cold\", \"warm\"]]\nTarget names (output-major): [\"target\"]\nMembers:\n manifest.json: validated (no self-integrity record)\n model.skops: 15011 bytes sha256=b332c1e060939f16a2f300797c6ee1625e99d684f215e45ba8772fd7dfee5da2\n schema.json: 388 bytes sha256=e770ad53af881faf368c686de86b2b00f0a64a1444e241985643349bf7b0bba4\n structure.json: 511 bytes sha256=8cf658be94b826ba4f81f1a78ed2c8e5107fce656d212dc888bd8621a9068baa\nTraining: {\"python\": \"3.14.7\", \"implementation\": \"CPython\", \"os\": \"Windows\", \"os_version\": \"11\", \"architecture\": \"AMD64\"}\nPackages: {\"catboost\": \"1.2.10\", \"mlbundle\": \"0.2.1\", \"mlschema\": \"0.3.0\", \"numpy\": \"2.5.4\", \"onnx\": \"1.23.0\", \"onnxruntime\": \"1.30.0\", \"pandas\": \"3.0.6\", \"scikit-learn\": \"1.9.1\", \"scipy\": \"1.18.1\", \"skops\": \"0.16.0\", \"xgboost\": \"3.4.1\"}\nONNX environment: null\nPresent: schema=yes, structure=yes, samples=no\nUser metadata: {}", "stderr": "" }, "editedVerify": { diff --git a/playground/README.md b/playground/README.md index 66acc0f..8d27486 100644 --- a/playground/README.md +++ b/playground/README.md @@ -8,7 +8,7 @@ Intended host: . Documentation: None: import mlbundle - assert mlbundle.__version__ == "0.2.0" + assert mlbundle.__version__ == "0.2.1" view = mlbundle.inspect(path) assert view.manifest.model.library == "sklearn" assert "cannot attest" in _console("--help") diff --git a/uv.lock b/uv.lock index 2e6358f..29e8825 100644 --- a/uv.lock +++ b/uv.lock @@ -459,7 +459,7 @@ wheels = [ [[package]] name = "mlbundle" -version = "0.2.0" +version = "0.2.1" source = { editable = "." } dependencies = [ { name = "pydantic" },